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Structured Latent Factor Analysis

by Yunlong He, Koray Kavukcuoglu, Yanjun Qi, Haesun Park
"... Latent factor models (LFMs) are a set of unsupervised methods that model observed high-dimensional data examples by linear combination of latent factors. To enable efficient pro-cessing of large data collections, LFMs aim to find concise descriptions of the members of a data collection while preserv ..."
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Latent factor models (LFMs) are a set of unsupervised methods that model observed high-dimensional data examples by linear combination of latent factors. To enable efficient pro-cessing of large data collections, LFMs aim to find concise descriptions of the members of a data collection while

Semiparametric Latent Factor Models

by Yee Whye Teh, Matthias Seeger - Workshop on Artificial Intelligence and Statistics 10 , 2005
"... We propose a semiparametric model for regression problems involving multiple response variables. The model makes use of a set of Gaussian processes that are linearly mixed to capture dependencies that may exist among the response variables. We propose an efficient approximate inference scheme for th ..."
Abstract - Cited by 78 (6 self) - Add to MetaCart
We propose a semiparametric model for regression problems involving multiple response variables. The model makes use of a set of Gaussian processes that are linearly mixed to capture dependencies that may exist among the response variables. We propose an efficient approximate inference scheme for this semiparametric model whose complexity is linear in the number of training data points. We present experimental results in the domain of multi-joint

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

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 1225 (10 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

Semiparametric latent factor models

by Matthias Seeger, Yee-whye Teh, Michael I. Jordan - Workshop on Artificial Intelligence and Statistics 10 , 2004
"... We propose a semiparametric model for regression and classification problems involving multiple response variables. The model makes use of a set of Gaussian processes to model the relationship to the inputs in a nonparametric fashion. Conditional dependencies between the responses can be captured th ..."
Abstract - Cited by 10 (4 self) - Add to MetaCart
is linear in the number of training data points. 1 Semiparametric Latent Factor Models We are interested in predicting multiple responses yc ∈ Yc, c = 1,..., C from covariates x ∈ X, and we would like to model the responses as conditionally dependent. In statistical terminology, we would like to “share

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

Learning the dependency structure of latent factors

by Yunlong He , Yanjun Qi , Koray Kavukcuoglu , Haesun Park - In Advances in Neural Information Processing Systems 25 , 2012
"... Abstract In this paper, we study latent factor models with dependency structure in the latent space. We propose a general learning framework which induces sparsity on the undirected graphical model imposed on the vector of latent factors. A novel latent factor model SLFA is then proposed as a matri ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract In this paper, we study latent factor models with dependency structure in the latent space. We propose a general learning framework which induces sparsity on the undirected graphical model imposed on the vector of latent factors. A novel latent factor model SLFA is then proposed as a

A Latent Factor Approach

by A Lessandro Viv Iani
"... ip ..."
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Abstract not found

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, Alexander H. Witzki, Amy Howerter, Tor D. Wager - COGNIT PSYCHOL , 2000
"... This individual differences study examined the separability of three often postulated executive functions—mental set shifting ("Shifting"), information updating and monitoring ("Updating"), and inhibition of prepotent responses ("Inhibition")—and their roles in complex ..."
Abstract - Cited by 696 (9 self) - Add to MetaCart
Sorting Test (WCST), Tower of Hanoi (TOH), 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 separable. Moreover, structural equation modeling

Latent

by Jia Guo, Melanie Wall , 2005
"... class regression on latent factors ..."
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class regression on latent factors
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