text stringlengths 0 4.09k |
|---|
Title: A Unified Semi-Supervised Dimensionality Reduction Framework for Manifold Learning |
Abstract: We present a general framework of semi-supervised dimensionality reduction for manifold learning which naturally generalizes existing supervised and unsupervised learning frameworks which apply the spectral decomposition. Algorithms derived under our framework are able to employ both labeled and unlabeled exa... |
Title: Testing for Homogeneity with Kernel Fisher Discriminant Analysis |
Abstract: We propose to investigate test statistics for testing homogeneity in reproducing kernel Hilbert spaces. Asymptotic null distributions under null hypothesis are derived, and consistency against fixed and local alternatives is assessed. Finally, experimental evidence of the performance of the proposed approach ... |
Title: A Semi-Automatic Framework to Discover Epistemic Modalities in Scientific Articles |
Abstract: Documents in scientific newspapers are often marked by attitudes and opinions of the author and/or other persons, who contribute with objective and subjective statements and arguments as well. In this respect, the attitude is often accomplished by a linguistic modality. As in languages like english, french an... |
Title: Estimation of Ambiguity Functions With Limited Spread |
Abstract: This paper proposes a new estimation procedure for the ambiguity function of a non-stationary time series. The stochastic properties of the empirical ambiguity function calculated from a single sample in time are derived. Different thresholding procedures are introduced for the estimation of the ambiguity fun... |
Title: Discrete schemes for Gaussian curvature and their convergence |
Abstract: In this paper, several discrete schemes for Gaussian curvature are surveyed. The convergence property of a modified discrete scheme for the Gaussian curvature is considered. Furthermore, a new discrete scheme for Gaussian curvature is resented. We prove that the new scheme converges at the regular vertex with... |
Title: Sliced Inverse Moment Regression Using Weighted Chi-Squared Tests for Dimension Reduction |
Abstract: We propose a new method for dimension reduction in regression using the first two inverse moments. We develop corresponding weighted chi-squared tests for the dimension of the regression. The proposed method considers linear combinations of Sliced Inverse Regression (SIR) and the method using a new candidate ... |
Title: Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis (book review) |
Abstract: Review of: Brigitte Le Roux and Henry Rouanet, Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis, Kluwer, Dordrecht, 2004, xi+475 pp. |
Title: Bolasso: model consistent Lasso estimation through the bootstrap |
Abstract: We consider the least-square linear regression problem with regularization by the l1-norm, a problem usually referred to as the Lasso. In this paper, we present a detailed asymptotic analysis of model consistency of the Lasso. For various decays of the regularization parameter, we compute asymptotic equivalen... |
Title: On the underestimation of model uncertainty by Bayesian K-nearest neighbors |
Abstract: When using the K-nearest neighbors method, one often ignores uncertainty in the choice of K. To account for such uncertainty, Holmes and Adams (2002) proposed a Bayesian framework for K-nearest neighbors (KNN). Their Bayesian KNN (BKNN) approach uses a pseudo-likelihood function, and standard Markov chain Mon... |
Title: Coverage Probability of Wald Interval for Binomial Parameters |
Abstract: In this paper, we develop an exact method for computing the minimum coverage probability of Wald interval for estimation of binomial parameters. Similar approach can be used for other type of confidence intervals. |
Title: Optimal Explicit Binomial Confidence Interval with Guaranteed Coverage Probability |
Abstract: In this paper, we develop an approach for optimizing the explicit binomial confidence interval recently derived by Chen et al. The optimization reduces conservativeness while guaranteeing prescribed coverage probability. |
Title: A $O(\log m)$, deterministic, polynomial-time computable approximation of Lewis Carroll's scoring rule |
Abstract: We provide deterministic, polynomial-time computable voting rules that approximate Dodgson's and (the ``minimization version'' of) Young's scoring rules to within a logarithmic factor. Our approximation of Dodgson's rule is tight up to a constant factor, as Dodgson's rule is $\NP$-hard to approximate to withi... |
Title: On Kernelization of Supervised Mahalanobis Distance Learners |
Abstract: This paper focuses on the problem of kernelizing an existing supervised Mahalanobis distance learner. The following features are included in the paper. Firstly, three popular learners, namely, "neighborhood component analysis", "large margin nearest neighbors" and "discriminant neighborhood embedding", which ... |
Title: Fast k Nearest Neighbor Search using GPU |
Abstract: The recent improvements of graphics processing units (GPU) offer to the computer vision community a powerful processing platform. Indeed, a lot of highly-parallelizable computer vision problems can be significantly accelerated using GPU architecture. Among these algorithms, the k nearest neighbor search (KNN)... |
Title: The Choquet integral for the aggregation of interval scales in multicriteria decision making |
Abstract: This paper addresses the question of which models fit with information concerning the preferences of the decision maker over each attribute, and his preferences about aggregation of criteria (interacting criteria). We show that the conditions induced by these information plus some intuitive conditions lead to... |
Title: Mathematical analysis of long tail economy using stochastic ranking processes |
Abstract: We present a new method of estimating the distribution of sales rates of, e.g., book titles at an online bookstore, from the time evolution of ranking data found at websites of the store. The method is based on new mathematical results on an infinite particle limit of the stochastic ranking process, and is su... |
Title: Linear Time Recognition Algorithms for Topological Invariants in 3D |
Abstract: In this paper, we design linear time algorithms to recognize and determine topological invariants such as the genus and homology groups in 3D. These properties can be used to identify patterns in 3D image recognition. This has tremendous amount of applications in 3D medical image analysis. Our method is based... |
Title: Towards Physarum robots: computing and manipulating on water surface |
Abstract: Plasmodium of Physarym polycephalum is an ideal biological substrate for implementing concurrent and parallel computation, including combinatorial geometry and optimization on graphs. We report results of scoping experiments on Physarum computing in conditions of minimal friction, on the water surface. We sho... |
Title: A constructive proof of the existence of Viterbi processes |
Abstract: Since the early days of digital communication, hidden Markov models (HMMs) have now been also routinely used in speech recognition, processing of natural languages, images, and in bioinformatics. In an HMM $(X_i,Y_i)_i\ge 1$, observations $X_1,X_2,...$ are assumed to be conditionally independent given an ``ex... |
Title: From Qualitative to Quantitative Proofs of Security Properties Using First-Order Conditional Logic |
Abstract: A first-order conditional logic is considered, with semantics given by a variant of epsilon-semantics, where p -> q means that Pr(q | p) approaches 1 super-polynomially --faster than any inverse polynomial. This type of convergence is needed for reasoning about security protocols. A complete axiomatization is... |
Title: On central tendency and dispersion measures for intervals and hypercubes |
Abstract: The uncertainty or the variability of the data may be treated by considering, rather than a single value for each data, the interval of values in which it may fall. This paper studies the derivation of basic description statistics for interval-valued datasets. We propose a geometrical approach in the determin... |
Title: Theory and Applications of Two-dimensional, Null-boundary, Nine-Neighborhood, Cellular Automata Linear rules |
Abstract: This paper deals with the theory and application of 2-Dimensional, nine-neighborhood, null- boundary, uniform as well as hybrid Cellular Automata (2D CA) linear rules in image processing. These rules are classified into nine groups depending upon the number of neighboring cells influences the cell under consi... |
Title: Information filtering based on wiki index database |
Abstract: In this paper we present a profile-based approach to information filtering by an analysis of the content of text documents. The Wikipedia index database is created and used to automatically generate the user profile from the user document collection. The problem-oriented Wikipedia subcorpora are created (usin... |
Title: Causal models have no complete axiomatic characterization |
Abstract: Markov networks and Bayesian networks are effective graphic representations of the dependencies embedded in probabilistic models. It is well known that independencies captured by Markov networks (called graph-isomorphs) have a finite axiomatic characterization. This paper, however, shows that independencies c... |
Title: Bayesian Inference on Mixtures of Distributions |
Abstract: This survey covers state-of-the-art Bayesian techniques for the estimation of mixtures. It complements the earlier Marin, Mengersen and Robert (2005) by studying new types of distributions, the multinomial, latent class and t distributions. It also exhibits closed form solutions for Bayesian inference in some... |
Title: Approximating the marginal likelihood in mixture models |
Abstract: In Chib (1995), a method for approximating marginal densities in a Bayesian setting is proposed, with one proeminent application being the estimation of the number of components in a normal mixture. As pointed out in Neal (1999) and Fruhwirth-Schnatter (2004), the approximation often fails short of providing ... |
Title: Boosting Algorithms: Regularization, Prediction and Model Fitting |
Abstract: We present a statistical perspective on boosting. Special emphasis is given to estimating potentially complex parametric or nonparametric models, including generalized linear and additive models as well as regression models for survival analysis. Concepts of degrees of freedom and corresponding Akaike or Baye... |
Title: Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting |
Abstract: The authors are doing the readers of Statistical Science a true service with a well-written and up-to-date overview of boosting that originated with the seminal algorithms of Freund and Schapire. Equally, we are grateful for high-level software that will permit a larger readership to experiment with, or simpl... |
Title: Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting |
Abstract: Comment on ``Boosting Algorithms: Regularization, Prediction and Model Fitting'' [arXiv:0804.2752] |
Title: Rejoinder: Boosting Algorithms: Regularization, Prediction and Model Fitting |
Abstract: Rejoinder to ``Boosting Algorithms: Regularization, Prediction and Model Fitting'' [arXiv:0804.2752] |
Title: An Analysis of Key Factors for the Success of the Communal Management of Knowledge |
Abstract: This paper explores the links between Knowledge Management and new community-based models of the organization from both a theoretical and an empirical perspective. From a theoretical standpoint, we look at Communities of Practice (CoPs) and Knowledge Management (KM) and explore the links between the two as th... |
Title: Information Preserving Component Analysis: Data Projections for Flow Cytometry Analysis |
Abstract: Flow cytometry is often used to characterize the malignant cells in leukemia and lymphoma patients, traced to the level of the individual cell. Typically, flow cytometric data analysis is performed through a series of 2-dimensional projections onto the axes of the data set. Through the years, clinicians have ... |
Title: Margin-adaptive model selection in statistical learning |
Abstract: A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.