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Structured Latent Factor Analysis
"... 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
- 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 ..."
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Cited by 78 (6 self)
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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
- 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 ..."
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Cited by 3779 (35 self)
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. 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
, 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 ..."
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Cited by 1225 (10 self)
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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
- 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
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Cited by 10 (4 self)
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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
- 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 ..."
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Cited by 618 (4 self)
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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
- 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 ..."
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Cited by 1 (0 self)
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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
The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent variable analysis
- 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 ..."
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Cited by 696 (9 self)
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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
Results 1 - 10
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3,684