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Title: Robust Recovery of Subspace Structures by Low-Rank Representation |
Abstract: In this work we address the subspace recovery problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to segment the samples into their respective subspaces and correct the possible errors as well. To this end, we propose a novel method termed Low-Ran... |
Title: Twitter mood predicts the stock market |
Abstract: Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e., can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of econo... |
Title: Making Tensor Factorizations Robust to Non-Gaussian Noise |
Abstract: Tensors are multi-way arrays, and the Candecomp/Parafac (CP) tensor factorization has found application in many different domains. The CP model is typically fit using a least squares objective function, which is a maximum likelihood estimate under the assumption of i.i.d. Gaussian noise. We demonstrate that t... |
Title: Near-Optimal Bayesian Active Learning with Noisy Observations |
Abstract: We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypothesis sampled from a known prior distribution. In the case of noise-free observations, a greedy algorithm called generalized binary... |
Title: Introduction to the iDian |
Abstract: The iDian (previously named as the Operation Agent System) is a framework designed to enable computer users to operate software in natural language. Distinct from current speech-recognition systems, our solution supports format-free combinations of orders, and is open to both developers and customers. We used... |
Title: Estimating animal densities and home range in regions with irregular boundaries and holes: a lattice-based alternative to the kernel density estimator |
Abstract: Density estimates based on point processes are often restrained to regions with irregular boundaries or holes. We propose a density estimator, the lattice-based density estimator, which produces reasonable density estimates under these circumstances. The estimation process starts with overlaying the region wi... |
Title: Exact block-wise optimization in group lasso and sparse group lasso for linear regression |
Abstract: The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the group lasso estimator either use gradient projection methods to update the entire coefficient vector simultane... |
Title: Local shrinkage rules, Levy processes, and regularized regression |
Abstract: We use Levy processes to generate joint prior distributions, and therefore penalty functions, for a location parameter as p grows large. This generalizes the class of local-global shrinkage rules based on scale mixtures of normals, illuminates new connections among disparate methods, and leads to new results ... |
Title: Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview |
Abstract: We consider the problem of learning about and comparing the consequences of dynamic treatment strategies on the basis of observational data. We formulate this within a probabilistic decision-theoretic framework. Our approach is compared with related work by Robins and others: in particular, we show how Robins... |
Title: Hybrid Linear Modeling via Local Best-fit Flats |
Abstract: We present a simple and fast geometric method for modeling data by a union of affine subspaces. The method begins by forming a collection of local best-fit affine subspaces, i.e., subspaces approximating the data in local neighborhoods. The correct sizes of the local neighborhoods are determined automatically... |
Title: Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition |
Abstract: Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of these bases. The applicability of these methods to visual object recognition tasks has been limited because of the prohibitive cost ... |
Title: Hardness Results for Agnostically Learning Low-Degree Polynomial Threshold Functions |
Abstract: Hardness results for maximum agreement problems have close connections to hardness results for proper learning in computational learning theory. In this paper we prove two hardness results for the problem of finding a low degree polynomial threshold function (PTF) which has the maximum possible agreement with... |
Title: Forecasting with Neural Networks: A comparative study using the data of emergency service |
Abstract: This is a case study discussing the supervised artificial neural network for the purpose of forecasting with comparison of the Box-Jenkins methodology by using the data of well known emergency service Rescue 1122. We fits a variety of neural network (NN) models and many problems were revealed while fitting th... |
Title: Mining Knowledge in Astrophysical Massive Data Sets |
Abstract: Modern scientific data mainly consist of huge datasets gathered by a very large number of techniques and stored in very diversified and often incompatible data repositories. More in general, in the e-science environment, it is considered as a critical and urgent requirement to integrate services across distri... |
Title: Random Projection Trees Revisited |
Abstract: The Random Projection Tree structures proposed in [Freund-Dasgupta STOC08] are space partitioning data structures that automatically adapt to various notions of intrinsic dimensionality of data. We prove new results for both the RPTreeMax and the RPTreeMean data structures. Our result for RPTreeMax gives a ne... |
Title: Joint interpretation of on-board vision and static GPS cartography for determination of correct speed limit |
Abstract: We present here a first prototype of a "Speed Limit Support" Advance Driving Assistance System (ADAS) producing permanent reliable information on the current speed limit applicable to the vehicle. Such a module can be used either for information of the driver, or could even serve for automatic setting of the ... |
Title: 3-D Rigid Models from Partial Views - Global Factorization |
Abstract: The so-called factorization methods recover 3-D rigid structure from motion by factorizing an observation matrix that collects 2-D projections of features. These methods became popular due to their robustness - they use a large number of views, which constrains adequately the solution - and computational simp... |
Title: Maximum Likelihood Mosaics |
Abstract: The majority of the approaches to the automatic recovery of a panoramic image from a set of partial views are suboptimal in the sense that the input images are aligned, or registered, pair by pair, e.g., consecutive frames of a video clip. These approaches lead to propagation errors that may be very severe, p... |
Title: Non-Euclidean statistical analysis of covariance matrices and diffusion tensors |
Abstract: The statistical analysis of covariance matrices occurs in many important applications, e.g. in diffusion tensor imaging and longitudinal data analysis. We consider the situation where it is of interest to estimate an average covariance matrix, describe its anisotropy, to carry out principal geodesic analysis ... |
Title: ANSIG - An Analytic Signature for Arbitrary 2D Shapes (or Bags of Unlabeled Points) |
Abstract: In image analysis, many tasks require representing two-dimensional (2D) shape, often specified by a set of 2D points, for comparison purposes. The challenge of the representation is that it must not only capture the characteristics of the shape but also be invariant to relevant transformations. Invariance to ... |
Title: Efficient Matrix Completion with Gaussian Models |
Abstract: A general framework based on Gaussian models and a MAP-EM algorithm is introduced in this paper for solving matrix/table completion problems. The numerical experiments with the standard and challenging movie ratings data show that the proposed approach, based on probably one of the simplest probabilistic mode... |
Title: Multiplierless Modules for Forward and Backward Integer Wavelet Transform |
Abstract: This article is about the architecture of a lossless wavelet filter bank with reprogrammable logic. It is based on second generation of wavelets with a reduced of number of operations. A new basic structure for parallel architecture and modules to forward and backward integer discrete wavelet transform is pro... |
Title: Revisiting Complex Moments For 2D Shape Representation and Image Normalization |
Abstract: When comparing 2D shapes, a key issue is their normalization. Translation and scale are easily taken care of by removing the mean and normalizing the energy. However, defining and computing the orientation of a 2D shape is not so simple. In fact, although for elongated shapes the principal axis can be used to... |
Title: Convex Analysis and Optimization with Submodular Functions: a Tutorial |
Abstract: Set-functions appear in many areas of computer science and applied mathematics, such as machine learning, computer vision, operations research or electrical networks. Among these set-functions, submodular functions play an important role, similar to convex functions on vector spaces. In this tutorial, the the... |
Title: Maximum Likelihood Joint Tracking and Association in a Strong Clutter without Combinatorial Complexity |
Abstract: We have developed an efficient algorithm for the maximum likelihood joint tracking and association problem in a strong clutter for GMTI data. By using an iterative procedure of the dynamic logic process "from vague-to-crisp," the new tracker overcomes combinatorial complexity of tracking in highly-cluttered s... |
Title: Robust PCA via Outlier Pursuit |
Abstract: Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it is nevertheless plagued by a well-known, well-documented sensitivity to outliers. Recent work has considered the setting where each ... |
Title: Large-Scale Clustering Based on Data Compression |
Abstract: This paper considers the clustering problem for large data sets. We propose an approach based on distributed optimization. The clustering problem is formulated as an optimization problem of maximizing the classification gain. We show that the optimization problem can be reformulated and decomposed into small-... |
Title: Statistical Compressive Sensing of Gaussian Mixture Models |
Abstract: A new framework of compressive sensing (CS), namely statistical compressive sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribution and achieving accurate reconstruction on average, is introduced. For signals following a Gaussian distribution, with Gauss... |
Title: Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain |
Abstract: We develop results for the use of Lasso and Post-Lasso methods to form first-stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, $p$. Our results apply even when $p$ is much larger than the sample size, $n$. We show that the IV estimator based... |
Title: A Protocol for Self-Synchronized Duty-Cycling in Sensor Networks: Generic Implementation in Wiselib |
Abstract: In this work we present a protocol for self-synchronized duty-cycling in wireless sensor networks with energy harvesting capabilities. The protocol is implemented in Wiselib, a library of generic algorithms for sensor networks. Simulations are conducted with the sensor network simulator Shawn. They are based ... |
Title: Impact of Insurance for Operational Risk: Is it worthwhile to insure or be insured for severe losses? |
Abstract: Under the Basel II standards, the Operational Risk (OpRisk) advanced measurement approach allows a provision for reduction of capital as a result of insurance mitigation of up to 20%. This paper studies the behaviour of different insurance policies in the context of capital reduction for a range of possible e... |
Title: Sublinear Optimization for Machine Learning |
Abstract: We give sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions of these problems, such as SVDD, hard margin SVM, and L2-SVM, for whic... |
Title: A Comparison of Two Proximity Catch Digraph Families in Testing Spatial Clustering |
Abstract: We consider two parametrized random digraph families, namely, proportional-edge and central similarity proximity catch digraphs (PCDs) and compare the performance of these two PCD families in testing spatial point patterns. These PCD families are based on relative positions of data points from two classes and... |
Title: On the Foundations of Adversarial Single-Class Classification |
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