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3,100 | A Privacy-Aware Bayesian Approach for Combining Classifier and Cluster
Ensembles | cs.LG | This paper introduces a privacy-aware Bayesian approach that combines
ensembles of classifiers and clusterers to perform semi-supervised and
transductive learning. We consider scenarios where instances and their
classification/clustering results are distributed across different data sites
and have sharing restrictions.... | computer science |
3,101 | Fast Planar Correlation Clustering for Image Segmentation | cs.CV | We describe a new optimization scheme for finding high-quality correlation
clusterings in planar graphs that uses weighted perfect matching as a
subroutine. Our method provides lower-bounds on the energy of the optimal
correlation clustering that are typically fast to compute and tight in
practice. We demonstrate our a... | computer science |
3,102 | Efficient Point-to-Subspace Query in $\ell^1$ with Application to Robust
Object Instance Recognition | cs.CV | Motivated by vision tasks such as robust face and object recognition, we
consider the following general problem: given a collection of low-dimensional
linear subspaces in a high-dimensional ambient (image) space, and a query point
(image), efficiently determine the nearest subspace to the query in $\ell^1$
distance. In... | computer science |
3,103 | Recklessly Approximate Sparse Coding | cs.LG | It has recently been observed that certain extremely simple feature encoding
techniques are able to achieve state of the art performance on several standard
image classification benchmarks including deep belief networks, convolutional
nets, factored RBMs, mcRBMs, convolutional RBMs, sparse autoencoders and
several othe... | computer science |
3,104 | Discriminative Sparse Coding on Multi-Manifold for Data Representation
and Classification | cs.CV | Sparse coding has been popularly used as an effective data representation
method in various applications, such as computer vision, medical imaging and
bioinformatics, etc. However, the conventional sparse coding algorithms and its
manifold regularized variants (graph sparse coding and Laplacian sparse
coding), learn th... | computer science |
3,105 | Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix
Factorization | cs.LG | Nonnegative Matrix Factorization (NMF) has been continuously evolving in
several areas like pattern recognition and information retrieval methods. It
factorizes a matrix into a product of 2 low-rank non-negative matrices that
will define parts-based, and linear representation of nonnegative data.
Recently, Graph regula... | computer science |
3,106 | Training Effective Node Classifiers for Cascade Classification | cs.CV | Cascade classifiers are widely used in real-time object detection. Different
from conventional classifiers that are designed for a low overall
classification error rate, a classifier in each node of the cascade is required
to achieve an extremely high detection rate and moderate false positive rate.
Although there are ... | computer science |
3,107 | Unsupervised Feature Learning for low-level Local Image Descriptors | cs.CV | Unsupervised feature learning has shown impressive results for a wide range
of input modalities, in particular for object classification tasks in computer
vision. Using a large amount of unlabeled data, unsupervised feature learning
methods are utilized to construct high-level representations that are
discriminative en... | computer science |
3,108 | Barnes-Hut-SNE | cs.LG | The paper presents an O(N log N)-implementation of t-SNE -- an embedding
technique that is commonly used for the visualization of high-dimensional data
in scatter plots and that normally runs in O(N^2). The new implementation uses
vantage-point trees to compute sparse pairwise similarities between the input
data object... | computer science |
3,109 | Boltzmann Machines and Denoising Autoencoders for Image Denoising | stat.ML | Image denoising based on a probabilistic model of local image patches has
been employed by various researchers, and recently a deep (denoising)
autoencoder has been proposed by Burger et al. [2012] and Xie et al. [2012] as
a good model for this. In this paper, we propose that another popular family of
models in the fie... | computer science |
3,110 | Pushing Stochastic Gradient towards Second-Order Methods --
Backpropagation Learning with Transformations in Nonlinearities | cs.LG | Recently, we proposed to transform the outputs of each hidden neuron in a
multi-layer perceptron network to have zero output and zero slope on average,
and use separate shortcut connections to model the linear dependencies instead.
We continue the work by firstly introducing a third transformation to normalize
the scal... | computer science |
3,111 | Deep Predictive Coding Networks | cs.LG | The quality of data representation in deep learning methods is directly
related to the prior model imposed on the representations; however, generally
used fixed priors are not capable of adjusting to the context in the data. To
address this issue, we propose deep predictive coding networks, a hierarchical
generative mo... | computer science |
3,112 | Kernelized Locality-Sensitive Hashing for Semi-Supervised Agglomerative
Clustering | cs.LG | Large scale agglomerative clustering is hindered by computational burdens. We
propose a novel scheme where exact inter-instance distance calculation is
replaced by the Hamming distance between Kernelized Locality-Sensitive Hashing
(KLSH) hashed values. This results in a method that drastically decreases
computation tim... | computer science |
3,113 | Learning Graphical Models of Images, Videos and Their Spatial
Transformations | cs.CV | Mixtures of Gaussians, factor analyzers (probabilistic PCA) and hidden Markov
models are staples of static and dynamic data modeling and image and video
modeling in particular. We show how topographic transformations in the input,
such as translation and shearing in images, can be accounted for in these
models by inclu... | computer science |
3,114 | On the Product Rule for Classification Problems | cs.LG | We discuss theoretical aspects of the product rule for classification
problems in supervised machine learning for the case of combining classifiers.
We show that (1) the product rule arises from the MAP classifier supposing
equivalent priors and conditional independence given a class; (2) under some
conditions, the pro... | computer science |
3,115 | Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity
Estimation from Facial Images | cs.CV | We propose a novel method for automatic pain intensity estimation from facial
images based on the framework of kernel Conditional Ordinal Random Fields
(KCORF). We extend this framework to account for heteroscedasticity on the
output labels(i.e., pain intensity scores) and introduce a novel dynamic
features, dynamic ra... | computer science |
3,116 | An improvement to k-nearest neighbor classifier | cs.CV | K-Nearest neighbor classifier (k-NNC) is simple to use and has little design
time like finding k values in k-nearest neighbor classifier, hence these are
suitable to work with dynamically varying data-sets. There exists some
fundamental improvements over the basic k-NNC, like weighted k-nearest
neighbors classifier (wh... | computer science |
3,117 | Improved Performance of Unsupervised Method by Renovated K-Means | cs.LG | Clustering is a separation of data into groups of similar objects. Every
group called cluster consists of objects that are similar to one another and
dissimilar to objects of other groups. In this paper, the K-Means algorithm is
implemented by three distance functions and to identify the optimal distance
function for c... | computer science |
3,118 | Sparse Coding and Dictionary Learning for Symmetric Positive Definite
Matrices: A Kernel Approach | cs.LG | Recent advances suggest that a wide range of computer vision problems can be
addressed more appropriately by considering non-Euclidean geometry. This paper
tackles the problem of sparse coding and dictionary learning in the space of
symmetric positive definite matrices, which form a Riemannian manifold. With
the aid of... | computer science |
3,119 | Distributed Low-rank Subspace Segmentation | cs.CV | Vision problems ranging from image clustering to motion segmentation to
semi-supervised learning can naturally be framed as subspace segmentation
problems, in which one aims to recover multiple low-dimensional subspaces from
noisy and corrupted input data. Low-Rank Representation (LRR), a convex
formulation of the subs... | computer science |
3,120 | Emotional Expression Classification using Time-Series Kernels | cs.CV | Estimation of facial expressions, as spatio-temporal processes, can take
advantage of kernel methods if one considers facial landmark positions and
their motion in 3D space. We applied support vector classification with kernels
derived from dynamic time-warping similarity measures. We achieved over 99%
accuracy - measu... | computer science |
3,121 | Non-parametric Power-law Data Clustering | cs.LG | It has always been a great challenge for clustering algorithms to
automatically determine the cluster numbers according to the distribution of
datasets. Several approaches have been proposed to address this issue,
including the recent promising work which incorporate Bayesian Nonparametrics
into the $k$-means clusterin... | computer science |
3,122 | Learning to encode motion using spatio-temporal synchrony | cs.CV | We consider the task of learning to extract motion from videos. To this end,
we show that the detection of spatial transformations can be viewed as the
detection of synchrony between the image sequence and a sequence of features
undergoing the motion we wish to detect. We show that learning about synchrony
is possible ... | computer science |
3,123 | Hyperparameter Optimization and Boosting for Classifying Facial
Expressions: How good can a "Null" Model be? | cs.CV | One of the goals of the ICML workshop on representation and learning is to
establish benchmark scores for a new data set of labeled facial expressions.
This paper presents the performance of a "Null" model consisting of
convolutions with random weights, PCA, pooling, normalization, and a linear
readout. Our approach fo... | computer science |
3,124 | Efficient Exact Inference in Planar Ising Models | cs.LG | We give polynomial-time algorithms for the exact computation of lowest-energy
(ground) states, worst margin violators, log partition functions, and marginal
edge probabilities in certain binary undirected graphical models. Our approach
provides an interesting alternative to the well-known graph cut paradigm in
that it ... | computer science |
3,125 | Compact Relaxations for MAP Inference in Pairwise MRFs with Piecewise
Linear Priors | cs.CV | Label assignment problems with large state spaces are important tasks
especially in computer vision. Often the pairwise interaction (or smoothness
prior) between labels assigned at adjacent nodes (or pixels) can be described
as a function of the label difference. Exact inference in such labeling tasks
is still difficul... | computer science |
3,126 | Axioms for graph clustering quality functions | cs.CV | We investigate properties that intuitively ought to be satisfied by graph
clustering quality functions, that is, functions that assign a score to a
clustering of a graph. Graph clustering, also known as network community
detection, is often performed by optimizing such a function. Two axioms
tailored for graph clusteri... | computer science |
3,127 | Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with
Implicit Low-rank Transformations | cs.CV | Images seen during test time are often not from the same distribution as
images used for learning. This problem, known as domain shift, occurs when
training classifiers from object-centric internet image databases and trying to
apply them directly to scene understanding tasks. The consequence is often
severe performanc... | computer science |
3,128 | Group-Sparse Signal Denoising: Non-Convex Regularization, Convex
Optimization | cs.CV | Convex optimization with sparsity-promoting convex regularization is a
standard approach for estimating sparse signals in noise. In order to promote
sparsity more strongly than convex regularization, it is also standard practice
to employ non-convex optimization. In this paper, we take a third approach. We
utilize a no... | computer science |
3,129 | On landmark selection and sampling in high-dimensional data analysis | stat.ML | In recent years, the spectral analysis of appropriately defined kernel
matrices has emerged as a principled way to extract the low-dimensional
structure often prevalent in high-dimensional data. Here we provide an
introduction to spectral methods for linear and nonlinear dimension reduction,
emphasizing ways to overcom... | computer science |
3,130 | Unsupervised Detection and Tracking of Arbitrary Objects with Dependent
Dirichlet Process Mixtures | stat.ML | This paper proposes a technique for the unsupervised detection and tracking
of arbitrary objects in videos. It is intended to reduce the need for detection
and localization methods tailored to specific object types and serve as a
general framework applicable to videos with varied objects, backgrounds, and
image qualiti... | computer science |
3,131 | A Slice Sampler for Restricted Hierarchical Beta Process with
Applications to Shared Subspace Learning | cs.LG | Hierarchical beta process has found interesting applications in recent years.
In this paper we present a modified hierarchical beta process prior with
applications to hierarchical modeling of multiple data sources. The novel use
of the prior over a hierarchical factor model allows factors to be shared
across different ... | computer science |
3,132 | Nested Dictionary Learning for Hierarchical Organization of Imagery and
Text | cs.LG | A tree-based dictionary learning model is developed for joint analysis of
imagery and associated text. The dictionary learning may be applied directly to
the imagery from patches, or to general feature vectors extracted from patches
or superpixels (using any existing method for image feature extraction). Each
image is ... | computer science |
3,133 | Clustering hidden Markov models with variational HEM | cs.LG | The hidden Markov model (HMM) is a widely-used generative model that copes
with sequential data, assuming that each observation is conditioned on the
state of a hidden Markov chain. In this paper, we derive a novel algorithm to
cluster HMMs based on the hierarchical EM (HEM) algorithm. The proposed
algorithm i) cluster... | computer science |
3,134 | A Multiscale Framework for Challenging Discrete Optimization | cs.CV | Current state-of-the-art discrete optimization methods struggle behind when
it comes to challenging contrast-enhancing discrete energies (i.e., favoring
different labels for neighboring variables). This work suggests a multiscale
approach for these challenging problems. Deriving an algebraic representation
allows us to... | computer science |
3,135 | Discrete Energy Minimization, beyond Submodularity: Applications and
Approximations | cs.CV | In this thesis I explore challenging discrete energy minimization problems
that arise mainly in the context of computer vision tasks. This work motivates
the use of such "hard-to-optimize" non-submodular functionals, and proposes
methods and algorithms to cope with the NP-hardness of their optimization.
Consequently, t... | computer science |
3,136 | Recognizing Static Signs from the Brazilian Sign Language: Comparing
Large-Margin Decision Directed Acyclic Graphs, Voting Support Vector Machines
and Artificial Neural Networks | cs.CV | In this paper, we explore and detail our experiments in a
high-dimensionality, multi-class image classification problem often found in
the automatic recognition of Sign Languages. Here, our efforts are directed
towards comparing the characteristics, advantages and drawbacks of creating and
training Support Vector Machi... | computer science |
3,137 | $l_1$-regularized Outlier Isolation and Regression | cs.CV | This paper proposed a new regression model called $l_1$-regularized outlier
isolation and regression (LOIRE) and a fast algorithm based on block coordinate
descent to solve this model. Besides, assuming outliers are gross errors
following a Bernoulli process, this paper also presented a Bernoulli estimate
model which, ... | computer science |
3,138 | On Classification with Bags, Groups and Sets | stat.ML | Many classification problems can be difficult to formulate directly in terms
of the traditional supervised setting, where both training and test samples are
individual feature vectors. There are cases in which samples are better
described by sets of feature vectors, that labels are only available for sets
rather than i... | computer science |
3,139 | Truncated Nuclear Norm Minimization for Image Restoration Based On
Iterative Support Detection | cs.CV | Recovering a large matrix from limited measurements is a challenging task
arising in many real applications, such as image inpainting, compressive
sensing and medical imaging, and this kind of problems are mostly formulated as
low-rank matrix approximation problems. Due to the rank operator being
non-convex and discont... | computer science |
3,140 | Convolutional Kernel Networks | cs.CV | An important goal in visual recognition is to devise image representations
that are invariant to particular transformations. In this paper, we address
this goal with a new type of convolutional neural network (CNN) whose
invariance is encoded by a reproducing kernel. Unlike traditional approaches
where neural networks ... | computer science |
3,141 | PRISM: Person Re-Identification via Structured Matching | cs.CV | Person re-identification (re-id), an emerging problem in visual surveillance,
deals with maintaining entities of individuals whilst they traverse various
locations surveilled by a camera network. From a visual perspective re-id is
challenging due to significant changes in visual appearance of individuals in
cameras wit... | computer science |
3,142 | Multi-stage Multi-task feature learning via adaptive threshold | cs.LG | Multi-task feature learning aims to identity the shared features among tasks
to improve generalization. It has been shown that by minimizing non-convex
learning models, a better solution than the convex alternatives can be
obtained. Therefore, a non-convex model based on the capped-$\ell_{1},\ell_{1}$
regularization wa... | computer science |
3,143 | Inner Product Similarity Search using Compositional Codes | cs.CV | This paper addresses the nearest neighbor search problem under inner product
similarity and introduces a compact code-based approach. The idea is to
approximate a vector using the composition of several elements selected from a
source dictionary and to represent this vector by a short code composed of the
indices of th... | computer science |
3,144 | An Open Source Pattern Recognition Toolbox for MATLAB | stat.ML | Pattern recognition and machine learning are becoming integral parts of
algorithms in a wide range of applications. Different algorithms and approaches
for machine learning include different tradeoffs between performance and
computation, so during algorithm development it is often necessary to explore a
variety of diff... | computer science |
3,145 | Fast, Robust and Non-convex Subspace Recovery | cs.LG | This work presents a fast and non-convex algorithm for robust subspace
recovery. The data sets considered include inliers drawn around a
low-dimensional subspace of a higher dimensional ambient space, and a possibly
large portion of outliers that do not lie nearby this subspace. The proposed
algorithm, which we refer t... | computer science |
3,146 | Recurrent Models of Visual Attention | cs.LG | Applying convolutional neural networks to large images is computationally
expensive because the amount of computation scales linearly with the number of
image pixels. We present a novel recurrent neural network model that is capable
of extracting information from an image or video by adaptively selecting a
sequence of ... | computer science |
3,147 | Further heuristics for $k$-means: The merge-and-split heuristic and the
$(k,l)$-means | cs.LG | Finding the optimal $k$-means clustering is NP-hard in general and many
heuristics have been designed for minimizing monotonically the $k$-means
objective. We first show how to extend Lloyd's batched relocation heuristic and
Hartigan's single-point relocation heuristic to take into account empty-cluster
and single-poin... | computer science |
3,148 | Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization | cs.LG | The problem of joint feature selection across a group of related tasks has
applications in many areas including biomedical informatics and computer
vision. We consider the l2,1-norm regularized regression model for joint
feature selection from multiple tasks, which can be derived in the
probabilistic framework by assum... | computer science |
3,149 | Learning Mixed Graphical Models | stat.ML | We consider the problem of learning the structure of a pairwise graphical
model over continuous and discrete variables. We present a new pairwise model
for graphical models with both continuous and discrete variables that is
amenable to structure learning. In previous work, authors have considered
structure learning of... | computer science |
3,150 | PAC-Bayesian Majority Vote for Late Classifier Fusion | stat.ML | A lot of attention has been devoted to multimedia indexing over the past few
years. In the literature, we often consider two kinds of fusion schemes: The
early fusion and the late fusion. In this paper we focus on late classifier
fusion, where one combines the scores of each modality at the decision level.
To tackle th... | computer science |
3,151 | Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann
Manifolds | cs.LG | Relationships between entities in datasets are often of multiple nature, like
geographical distance, social relationships, or common interests among people
in a social network, for example. This information can naturally be modeled by
a set of weighted and undirected graphs that form a global multilayer graph,
where th... | computer science |
3,152 | A Unified Framework for Probabilistic Component Analysis | cs.LG | We present a unifying framework which reduces the construction of
probabilistic component analysis techniques to a mere selection of the latent
neighbourhood, thus providing an elegant and principled framework for creating
novel component analysis models as well as constructing probabilistic
equivalents of deterministi... | computer science |
3,153 | $l_{2,p}$ Matrix Norm and Its Application in Feature Selection | cs.LG | Recently, $l_{2,1}$ matrix norm has been widely applied to many areas such as
computer vision, pattern recognition, biological study and etc. As an extension
of $l_1$ vector norm, the mixed $l_{2,1}$ matrix norm is often used to find
jointly sparse solutions. Moreover, an efficient iterative algorithm has been
designed... | computer science |
3,154 | Separable Dictionary Learning | cs.CV | Many techniques in computer vision, machine learning, and statistics rely on
the fact that a signal of interest admits a sparse representation over some
dictionary. Dictionaries are either available analytically, or can be learned
from a suitable training set. While analytic dictionaries permit to capture the
global st... | computer science |
3,155 | Sparse Projections of Medical Images onto Manifolds | cs.CV | Manifold learning has been successfully applied to a variety of medical
imaging problems. Its use in real-time applications requires fast projection
onto the low-dimensional space. To this end, out-of-sample extensions are
applied by constructing an interpolation function that maps from the input
space to the low-dimen... | computer science |
3,156 | A Diffusion Process on Riemannian Manifold for Visual Tracking | cs.CV | Robust visual tracking for long video sequences is a research area that has
many important applications. The main challenges include how the target image
can be modeled and how this model can be updated. In this paper, we model the
target using a covariance descriptor, as this descriptor is robust to problems
such as p... | computer science |
3,157 | Generalizing k-means for an arbitrary distance matrix | cs.LG | The original k-means clustering method works only if the exact vectors
representing the data points are known. Therefore calculating the distances
from the centroids needs vector operations, since the average of abstract data
points is undefined. Existing algorithms can be extended for those cases when
the sole input i... | computer science |
3,158 | Learning Transformations for Clustering and Classification | cs.CV | A low-rank transformation learning framework for subspace clustering and
classification is here proposed. Many high-dimensional data, such as face
images and motion sequences, approximately lie in a union of low-dimensional
subspaces. The corresponding subspace clustering problem has been extensively
studied in the lit... | computer science |
3,159 | Recovery guarantees for exemplar-based clustering | stat.ML | For a certain class of distributions, we prove that the linear programming
relaxation of $k$-medoids clustering---a variant of $k$-means clustering where
means are replaced by exemplars from within the dataset---distinguishes points
drawn from nonoverlapping balls with high probability once the number of points
drawn a... | computer science |
3,160 | Visual-Semantic Scene Understanding by Sharing Labels in a Context
Network | cs.CV | We consider the problem of naming objects in complex, natural scenes
containing widely varying object appearance and subtly different names.
Informed by cognitive research, we propose an approach based on sharing context
based object hypotheses between visual and lexical spaces. To this end, we
present the Visual Seman... | computer science |
3,161 | Latent Fisher Discriminant Analysis | cs.LG | Linear Discriminant Analysis (LDA) is a well-known method for dimensionality
reduction and classification. Previous studies have also extended the
binary-class case into multi-classes. However, many applications, such as
object detection and keyframe extraction cannot provide consistent
instance-label pairs, while LDA ... | computer science |
3,162 | Solving OSCAR regularization problems by proximal splitting algorithms | cs.CV | The OSCAR (octagonal selection and clustering algorithm for regression)
regularizer consists of a L_1 norm plus a pair-wise L_inf norm (responsible for
its grouping behavior) and was proposed to encourage group sparsity in
scenarios where the groups are a priori unknown. The OSCAR regularizer has a
non-trivial proximit... | computer science |
3,163 | A Unified Framework for Representation-based Subspace Clustering of
Out-of-sample and Large-scale Data | cs.LG | Under the framework of spectral clustering, the key of subspace clustering is
building a similarity graph which describes the neighborhood relations among
data points. Some recent works build the graph using sparse, low-rank, and
$\ell_2$-norm-based representation, and have achieved state-of-the-art
performance. Howeve... | computer science |
3,164 | Structured learning of sum-of-submodular higher order energy functions | cs.CV | Submodular functions can be exactly minimized in polynomial time, and the
special case that graph cuts solve with max flow \cite{KZ:PAMI04} has had
significant impact in computer vision
\cite{BVZ:PAMI01,Kwatra:SIGGRAPH03,Rother:GrabCut04}. In this paper we address
the important class of sum-of-submodular (SoS) function... | computer science |
3,165 | Empirical Evaluation of Rectified Activations in Convolutional Network | cs.LG | In this paper we investigate the performance of different types of rectified
activation functions in convolutional neural network: standard rectified linear
unit (ReLU), leaky rectified linear unit (Leaky ReLU), parametric rectified
linear unit (PReLU) and a new randomized leaky rectified linear units (RReLU).
We evalu... | computer science |
3,166 | Why Regularized Auto-Encoders learn Sparse Representation? | stat.ML | While the authors of Batch Normalization (BN) identify and address an
important problem involved in training deep networks-- \textit{Internal
Covariate Shift}-- the current solution has certain drawbacks. For instance, BN
depends on batch statistics for layerwise input normalization during training
which makes the esti... | computer science |
3,167 | Discrete Independent Component Analysis (DICA) with Belief Propagation | cs.CV | We apply belief propagation to a Bayesian bipartite graph composed of
discrete independent hidden variables and discrete visible variables. The
network is the Discrete counterpart of Independent Component Analysis (DICA)
and it is manipulated in a factor graph form for inference and learning. A full
set of simulations ... | computer science |
3,168 | CURL: Co-trained Unsupervised Representation Learning for Image
Classification | cs.LG | In this paper we propose a strategy for semi-supervised image classification
that leverages unsupervised representation learning and co-training. The
strategy, that is called CURL from Co-trained Unsupervised Representation
Learning, iteratively builds two classifiers on two different views of the
data. The two views c... | computer science |
3,169 | Online Open World Recognition | cs.CV | As we enter into the big data age and an avalanche of images have become
readily available, recognition systems face the need to move from close, lab
settings where the number of classes and training data are fixed, to dynamic
scenarios where the number of categories to be recognized grows continuously
over time, as we... | computer science |
3,170 | Active Learning for Online Recognition of Human Activities from
Streaming Videos | stat.ML | Recognising human activities from streaming videos poses unique challenges to
learning algorithms: predictive models need to be scalable, incrementally
trainable, and must remain bounded in size even when the data stream is
arbitrarily long. Furthermore, as parameter tuning is problematic in a
streaming setting, suitab... | computer science |
3,171 | Gaussian Process Domain Experts for Model Adaptation in Facial Behavior
Analysis | stat.ML | We present a novel approach for supervised domain adaptation that is based
upon the probabilistic framework of Gaussian processes (GPs). Specifically, we
introduce domain-specific GPs as local experts for facial expression
classification from face images. The adaptation of the classifier is
facilitated in probabilistic... | computer science |
3,172 | Recurrent Attentional Networks for Saliency Detection | cs.CV | Convolutional-deconvolution networks can be adopted to perform end-to-end
saliency detection. But, they do not work well with objects of multiple scales.
To overcome such a limitation, in this work, we propose a recurrent attentional
convolutional-deconvolution network (RACDNN). Using spatial transformer and
recurrent ... | computer science |
3,173 | Triplet Probabilistic Embedding for Face Verification and Clustering | cs.CV | Despite significant progress made over the past twenty five years,
unconstrained face verification remains a challenging problem. This paper
proposes an approach that couples a deep CNN-based approach with a
low-dimensional discriminative embedding learned using triplet probability
constraints to solve the unconstraine... | computer science |
3,174 | The Mean Partition Theorem of Consensus Clustering | cs.LG | To devise efficient solutions for approximating a mean partition in consensus
clustering, Dimitriadou et al. [3] presented a necessary condition of
optimality for a consensus function based on least square distances. We show
that their result is pivotal for deriving interesting properties of consensus
clustering beyond... | computer science |
3,175 | A New Approach in Persian Handwritten Letters Recognition Using Error
Correcting Output Coding | cs.CV | Classification Ensemble, which uses the weighed polling of outputs, is the
art of combining a set of basic classifiers for generating high-performance,
robust and more stable results. This study aims to improve the results of
identifying the Persian handwritten letters using Error Correcting Output
Coding (ECOC) ensemb... | computer science |
3,176 | Towards Conceptual Compression | stat.ML | We introduce a simple recurrent variational auto-encoder architecture that
significantly improves image modeling. The system represents the
state-of-the-art in latent variable models for both the ImageNet and Omniglot
datasets. We show that it naturally separates global conceptual information
from lower level details, ... | computer science |
3,177 | A Linear Approximation to the chi^2 Kernel with Geometric Convergence | cs.LG | We propose a new analytical approximation to the $\chi^2$ kernel that
converges geometrically. The analytical approximation is derived with
elementary methods and adapts to the input distribution for optimal convergence
rate. Experiments show the new approximation leads to improved performance in
image classification a... | computer science |
3,178 | On multi-view feature learning | cs.CV | Sparse coding is a common approach to learning local features for object
recognition. Recently, there has been an increasing interest in learning
features from spatio-temporal, binocular, or other multi-observation data,
where the goal is to encode the relationship between images rather than the
content of a single ima... | computer science |
3,179 | Manifold Relevance Determination | cs.LG | In this paper we present a fully Bayesian latent variable model which
exploits conditional nonlinear(in)-dependence structures to learn an efficient
latent representation. The latent space is factorized to represent shared and
private information from multiple views of the data. In contrast to previous
approaches, we i... | computer science |
3,180 | Total Variation and Euler's Elastica for Supervised Learning | cs.LG | In recent years, total variation (TV) and Euler's elastica (EE) have been
successfully applied to image processing tasks such as denoising and
inpainting. This paper investigates how to extend TV and EE to the supervised
learning settings on high dimensional data. The supervised learning problem can
be formulated as an... | computer science |
3,181 | Learning Efficient Structured Sparse Models | cs.LG | We present a comprehensive framework for structured sparse coding and
modeling extending the recent ideas of using learnable fast regressors to
approximate exact sparse codes. For this purpose, we develop a novel
block-coordinate proximal splitting method for the iterative solution of
hierarchical sparse coding problem... | computer science |
3,182 | Is margin preserved after random projection? | cs.LG | Random projections have been applied in many machine learning algorithms.
However, whether margin is preserved after random projection is non-trivial and
not well studied. In this paper we analyse margin distortion after random
projection, and give the conditions of margin preservation for binary
classification problem... | computer science |
3,183 | Dimensionality Reduction by Local Discriminative Gaussians | cs.LG | We present local discriminative Gaussian (LDG) dimensionality reduction, a
supervised dimensionality reduction technique for classification. The LDG
objective function is an approximation to the leave-one-out training error of a
local quadratic discriminant analysis classifier, and thus acts locally to each
training po... | computer science |
3,184 | Clustering by Low-Rank Doubly Stochastic Matrix Decomposition | cs.LG | Clustering analysis by nonnegative low-rank approximations has achieved
remarkable progress in the past decade. However, most approximation approaches
in this direction are still restricted to matrix factorization. We propose a
new low-rank learning method to improve the clustering performance, which is
beyond matrix f... | computer science |
3,185 | Statistical Translation, Heat Kernels and Expected Distances | cs.LG | High dimensional structured data such as text and images is often poorly
understood and misrepresented in statistical modeling. The standard histogram
representation suffers from high variance and performs poorly in general. We
explore novel connections between statistical translation, heat kernels on
manifolds and gra... | computer science |
3,186 | Learning Invariant Representations with Local Transformations | cs.LG | Learning invariant representations is an important problem in machine
learning and pattern recognition. In this paper, we present a novel framework
of transformation-invariant feature learning by incorporating linear
transformations into the feature learning algorithms. For example, we present
the transformation-invari... | computer science |
3,187 | Incorporating Domain Knowledge in Matching Problems via Harmonic
Analysis | cs.LG | Matching one set of objects to another is a ubiquitous task in machine
learning and computer vision that often reduces to some form of the quadratic
assignment problem (QAP). The QAP is known to be notoriously hard, both in
theory and in practice. Here, we investigate if this difficulty can be
mitigated when some addit... | computer science |
3,188 | Large Scale Variational Bayesian Inference for Structured Scale Mixture
Models | cs.CV | Natural image statistics exhibit hierarchical dependencies across multiple
scales. Representing such prior knowledge in non-factorial latent tree models
can boost performance of image denoising, inpainting, deconvolution or
reconstruction substantially, beyond standard factorial "sparse" methodology.
We derive a large ... | computer science |
3,189 | Deep Lambertian Networks | cs.CV | Visual perception is a challenging problem in part due to illumination
variations. A possible solution is to first estimate an illumination invariant
representation before using it for recognition. The object albedo and surface
normals are examples of such representations. In this paper, we introduce a
multilayer gener... | computer science |
3,190 | Small-sample Brain Mapping: Sparse Recovery on Spatially Correlated
Designs with Randomization and Clustering | cs.LG | Functional neuroimaging can measure the brain?s response to an external
stimulus. It is used to perform brain mapping: identifying from these
observations the brain regions involved. This problem can be cast into a linear
supervised learning task where the neuroimaging data are used as predictors for
the stimulus. Brai... | computer science |
3,191 | Learning Object Arrangements in 3D Scenes using Human Context | cs.LG | We consider the problem of learning object arrangements in a 3D scene. The
key idea here is to learn how objects relate to human poses based on their
affordances, ease of use and reachability. In contrast to modeling
object-object relationships, modeling human-object relationships scales
linearly in the number of objec... | computer science |
3,192 | Modeling Images using Transformed Indian Buffet Processes | cs.CV | Latent feature models are attractive for image modeling, since images
generally contain multiple objects. However, many latent feature models ignore
that objects can appear at different locations or require pre-segmentation of
images. While the transformed Indian buffet process (tIBP) provides a method
for modeling tra... | computer science |
3,193 | Training Support Vector Machines Using Frank-Wolfe Optimization Methods | cs.LG | Training a Support Vector Machine (SVM) requires the solution of a quadratic
programming problem (QP) whose computational complexity becomes prohibitively
expensive for large scale datasets. Traditional optimization methods cannot be
directly applied in these cases, mainly due to memory restrictions.
By adopting a sl... | computer science |
3,194 | Matching Pursuit LASSO Part II: Applications and Sparse Recovery over
Batch Signals | cs.CV | Matching Pursuit LASSIn Part I \cite{TanPMLPart1}, a Matching Pursuit LASSO
({MPL}) algorithm has been presented for solving large-scale sparse recovery
(SR) problems. In this paper, we present a subspace search to further improve
the performance of MPL, and then continue to address another major challenge of
SR -- bat... | computer science |
3,195 | A new framework for optimal classifier design | cs.CV | The use of alternative measures to evaluate classifier performance is gaining
attention, specially for imbalanced problems. However, the use of these
measures in the classifier design process is still unsolved. In this work we
propose a classifier designed specifically to optimize one of these alternative
measures, nam... | computer science |
3,196 | Revisiting Bayesian Blind Deconvolution | cs.CV | Blind deconvolution involves the estimation of a sharp signal or image given
only a blurry observation. Because this problem is fundamentally ill-posed,
strong priors on both the sharp image and blur kernel are required to
regularize the solution space. While this naturally leads to a standard MAP
estimation framework,... | computer science |
3,197 | A Supervised Neural Autoregressive Topic Model for Simultaneous Image
Classification and Annotation | cs.CV | Topic modeling based on latent Dirichlet allocation (LDA) has been a
framework of choice to perform scene recognition and annotation. Recently, a
new type of topic model called the Document Neural Autoregressive Distribution
Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance
for document mod... | computer science |
3,198 | Feature Selection Using Classifier in High Dimensional Data | cs.CV | Feature selection is frequently used as a pre-processing step to machine
learning. It is a process of choosing a subset of original features so that the
feature space is optimally reduced according to a certain evaluation criterion.
The central objective of this paper is to reduce the dimension of the data by
finding a... | computer science |
3,199 | Fast nonparametric clustering of structured time-series | cs.LG | In this publication, we combine two Bayesian non-parametric models: the
Gaussian Process (GP) and the Dirichlet Process (DP). Our innovation in the GP
model is to introduce a variation on the GP prior which enables us to model
structured time-series data, i.e. data containing groups where we wish to model
inter- and in... | computer science |
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