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Probabilistic Latent Semantic Analysis

by Thomas Hofmann - In Proc. of Uncertainty in Artificial Intelligence, UAI’99 , 1999
"... Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two--mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Sema ..."
Abstract - Cited by 760 (9 self) - Add to MetaCart
to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.

Probabilistic Latent Semantic Indexing

by Thomas Hofmann , 1999
"... Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized ..."
Abstract - Cited by 1207 (11 self) - Add to MetaCart
Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized

Probabilistic Principal Component Analysis

by Michael E. Tipping, Chris M. Bishop - Journal of the Royal Statistical Society, Series B , 1999
"... Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of paramet ..."
Abstract - Cited by 703 (5 self) - Add to MetaCart
of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss, with illustrative examples, the advantages conveyed by this probabilistic approach

Mixtures of Probabilistic Principal Component Analysers

by Michael E. Tipping, Christopher M. Bishop , 1998
"... Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a com ..."
Abstract - Cited by 537 (6 self) - Add to MetaCart
maximum-likelihood framework, based on a specific form of Gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context

Probabilistic Visual Learning for Object Representation

by Baback Moghaddam, Alex Pentland , 1996
"... We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of ..."
Abstract - Cited by 705 (15 self) - Add to MetaCart
-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection

A maximum likelihood approach to continuous speech recognition

by Lalit R. Bahl, Frederick Jelinek, Robert, L. Mercer - IEEE Trans. Pattern Anal. Machine Intell , 1983
"... Abstract-Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of sta-tistical models for use in speech recognition. We give special attention to determining the ..."
Abstract - Cited by 472 (9 self) - Add to MetaCart
Abstract-Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of sta-tistical models for use in speech recognition. We give special attention to determining

A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge

by Thomas K Landauer, Susan T. Dutnais - PSYCHOLOGICAL REVIEW , 1997
"... How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LS ..."
Abstract - Cited by 1772 (10 self) - Add to MetaCart
How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis

The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent variable analysis

by Akira Miyake, Naomi P. Friedman, Michael J. Emerson, Er H. Witzki, Amy Howerter, Tor D. Wager, John Duncan, Priti Shah - Cognit Psychol , 2000
"... This individual differences study examined the separability of three often postu-lated executive functions—mental set shifting (‘‘Shifting’’), information updating and monitoring (‘‘Updating’’), and inhibition of prepotent responses (‘‘Inhibi-tion’’)—and their roles in complex ‘‘frontal lobe’ ’ or ‘ ..."
Abstract - Cited by 626 (9 self) - Add to MetaCart
), random number generation (RNG), operation span, and dual tasking. Confirmatory factor analysis indicated that the three target executive functions are moderately correlated with one another, but are clearly sepa-rable. Moreover, structural equation modeling suggested that the three functions

A Bayesian method for the induction of probabilistic networks from data

by Gregory F. Cooper, EDWARD HERSKOVITS - MACHINE LEARNING , 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
Abstract - Cited by 1381 (32 self) - Add to MetaCart
of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief

Model-Based Clustering, Discriminant Analysis, and Density Estimation

by Chris Fraley, Adrian E. Raftery - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION , 2000
"... Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little ..."
Abstract - Cited by 557 (28 self) - Add to MetaCart
for model-based clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, mineeld detection, cluster
Results 1 - 10 of 135,119