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Subspace, Latent Structure and Feature Selection Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers / [electronic resource] : edited by Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor. - Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. - X, 209 p. Also available online. online resource. - Lecture Notes in Computer Science, 3940 0302-9743 ; . - Lecture Notes in Computer Science, 3940 .

Invited Contributions -- Discrete Component Analysis -- Overview and Recent Advances in Partial Least Squares -- Random Projection, Margins, Kernels, and Feature-Selection -- Some Aspects of Latent Structure Analysis -- Feature Selection for Dimensionality Reduction -- Contributed Papers -- Auxiliary Variational Information Maximization for Dimensionality Reduction -- Constructing Visual Models with a Latent Space Approach -- Is Feature Selection Still Necessary? -- Class-Specific Subspace Discriminant Analysis for High-Dimensional Data -- Incorporating Constraints and Prior Knowledge into Factorization Algorithms – An Application to 3D Recovery -- A Simple Feature Extraction for High Dimensional Image Representations -- Identifying Feature Relevance Using a Random Forest -- Generalization Bounds for Subspace Selection and Hyperbolic PCA -- Less Biased Measurement of Feature Selection Benefits.

ISBN: 9783540341383

Standard No.: 10.1007/11752790 doi

Subjects--Topical Terms:
Computer science.
Computer software.
Artificial intelligence.
Computer vision.
Optical pattern recognition.
Computer Science.
Algorithm Analysis and Problem Complexity.
Probability and Statistics in Computer Science.
Computation by Abstract Devices.
Artificial Intelligence (incl. Robotics).
Image Processing and Computer Vision.
Pattern Recognition.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 005.1