Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science
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Published By Springer Berlin Heidelberg

9783540341376, 9783540341383

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Auxiliary Variational Information Maximization for Dimensionality Reduction

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science ◽

10.1007/11752790_6 ◽

2006 ◽

pp. 103-114

Author(s):

Felix Agakov ◽

David Barber

Keyword(s):

Dimensionality Reduction ◽

Information Maximization

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Some Aspects of Latent Structure Analysis

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science ◽

10.1007/11752790_4 ◽

2006 ◽

pp. 69-83 ◽

Cited By ~ 2

Author(s):

D. M. Titterington

Keyword(s):

Structure Analysis ◽

Latent Structure ◽

Latent Structure Analysis

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Constructing Visual Models with a Latent Space Approach

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science ◽

10.1007/11752790_7 ◽

2006 ◽

pp. 115-126 ◽

Cited By ~ 1

Author(s):

Florent Monay ◽

Pedro Quelhas ◽

Daniel Gatica-Perez ◽

Jean-Marc Odobez

Keyword(s):

Visual Models ◽

Latent Space ◽

Space Approach

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Less Biased Measurement of Feature Selection Benefits

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science ◽

10.1007/11752790_14 ◽

2006 ◽

pp. 198-208 ◽

Cited By ~ 2

Author(s):

Juha Reunanen

Keyword(s):

Feature Selection

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Is Feature Selection Still Necessary?

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science ◽

10.1007/11752790_8 ◽

2006 ◽

pp. 127-138 ◽

Cited By ~ 5

Author(s):

Amir Navot ◽

Ran Gilad-Bachrach ◽

Yiftah Navot ◽

Naftali Tishby

Keyword(s):

Feature Selection

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Generalization Bounds for Subspace Selection and Hyperbolic PCA

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science ◽

10.1007/11752790_13 ◽

2006 ◽

pp. 185-197 ◽

Cited By ~ 2

Author(s):

Andreas Maurer

Keyword(s):

Generalization Bounds ◽

Subspace Selection

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Incorporating Constraints and Prior Knowledge into Factorization Algorithms – An Application to 3D Recovery

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science ◽

10.1007/11752790_10 ◽

2006 ◽

pp. 151-162 ◽

Cited By ~ 2

Author(s):

Amit Gruber ◽

Yair Weiss

Keyword(s):

Prior Knowledge ◽

Factorization Algorithms

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Random Projection, Margins, Kernels, and Feature-Selection

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science ◽

10.1007/11752790_3 ◽

2006 ◽

pp. 52-68 ◽

Cited By ~ 27

Author(s):

Avrim Blum

Keyword(s):

Feature Selection ◽

Random Projection

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Class-Specific Subspace Discriminant Analysis for High-Dimensional Data

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science ◽

10.1007/11752790_9 ◽

2006 ◽

pp. 139-150

Author(s):

Charles Bouveyron ◽

Stéphane Girard ◽

Cordelia Schmid

Keyword(s):

Discriminant Analysis ◽

High Dimensional Data ◽

High Dimensional

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Identifying Feature Relevance Using a Random Forest

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science ◽

10.1007/11752790_12 ◽

2006 ◽

pp. 173-184 ◽

Cited By ~ 18

Author(s):

Jeremy Rogers ◽

Steve Gunn

Keyword(s):

Random Forest ◽

Feature Relevance

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Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer ScienceLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

Auxiliary Variational Information Maximization for Dimensionality Reduction

Some Aspects of Latent Structure Analysis

Constructing Visual Models with a Latent Space Approach

Less Biased Measurement of Feature Selection Benefits

Is Feature Selection Still Necessary?

Generalization Bounds for Subspace Selection and Hyperbolic PCA

Incorporating Constraints and Prior Knowledge into Factorization Algorithms – An Application to 3D Recovery

Random Projection, Margins, Kernels, and Feature-Selection

Class-Specific Subspace Discriminant Analysis for High-Dimensional Data

Identifying Feature Relevance Using a Random Forest

Subspace, Latent Structure and Feature Selection - Lecture Notes in Computer Science
Latest Publications