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Maximum likelihood from incomplete data via the EM algorithm

by A. P. Dempster, N. M. Laird, D. B. Rubin - JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B , 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
Abstract - Cited by 11972 (17 self) - Add to MetaCart
situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.

Simplified Models with Factor Analysis Technique

by Yu-ju Lin, Chin-sheng Huang, Che-chern Lin
"... Abstract: In this paper, we use feed forward neural networks with the back-propagation algorithm to build decision models for five insurances including life, annuity, health, accident, and investment-oriented insurances. Six features (variables) were selected for the inputs of the neural networks in ..."
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phases: Phase 1 (Experiments 1 to 3) and Phase 2 (Experiments 4 to 6). In Phase 1, we used the six features as the inputs of the neural networks. In Phase 2, we employed the factor analysis method to select three more important features from the six features. In Phase 1, Experiment 1 used a single neural

MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS

by Yehuda Koren, Robert Bell, Chris Volinsky - IEEE COMPUTER , 2009
"... As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest-neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels. Modern co ..."
Abstract - Cited by 593 (4 self) - Add to MetaCart
As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest-neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels. Modern

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 709 (5 self) - Add to MetaCart
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

Indexing by latent semantic analysis

by Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, Richard Harshman - JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE , 1990
"... A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The p ..."
Abstract - Cited by 3779 (35 self) - Add to MetaCart
. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 or-thogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries

Data Mining: Concepts and Techniques

by Jiawei Han, Micheline Kamber , 2000
"... Our capabilities of both generating and collecting data have been increasing rapidly in the last several decades. Contributing factors include the widespread use of bar codes for most commercial products, the computerization of many business, scientific and government transactions and managements, a ..."
Abstract - Cited by 3142 (23 self) - Add to MetaCart
Our capabilities of both generating and collecting data have been increasing rapidly in the last several decades. Contributing factors include the widespread use of bar codes for most commercial products, the computerization of many business, scientific and government transactions and managements

Non-negative matrix factorization with sparseness constraints,”

by Patrik O Hoyer , Patrik Hoyer@helsinki , Fi - Journal of Machine Learning Research, , 2004
"... Abstract Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we sho ..."
Abstract - Cited by 498 (0 self) - Add to MetaCart
Abstract Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we

Evaluating the use of exploratory factor analysis in psychological research

by Leandre R. Fabrigar, Duane T. Wegener, Robert C. MacCallum, Erin J. Strahan - PSYCHOLOGICAL METHODS , 1999
"... Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of ..."
Abstract - Cited by 524 (4 self) - Add to MetaCart
Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each

Unsupervised Learning by Probabilistic Latent Semantic Analysis

by Thomas Hofmann - Machine Learning , 2001
"... Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co-occurren ..."
Abstract - Cited by 618 (4 self) - Add to MetaCart
Abstract. This paper presents a novel statistical method for factor analysis of binary and count data which is closely related to a technique known as Latent Semantic Analysis. In contrast to the latter method which stems from linear algebra and performs a Singular Value Decomposition of co

An Empirical Study of Smoothing Techniques for Language Modeling

by Stanley F. Chen , 1998
"... We present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by Jelinek and Mercer (1980), Katz (1987), and Church and Gale (1991). We investigate for the first time how factors such as training data size, corpus (e.g., Br ..."
Abstract - Cited by 1224 (21 self) - Add to MetaCart
We present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by Jelinek and Mercer (1980), Katz (1987), and Church and Gale (1991). We investigate for the first time how factors such as training data size, corpus (e
Results 1 - 10 of 229,484