Subspace, Latent Structure and Feature Selection Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers / edited by Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor.

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Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Saunders, Craig (Editor), Grobelnik, Marko (Editor), Gunn, Steve (Editor), Shawe-Taylor, John (Editor)
Format:	eBook
Language:	English
Published:	Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
Edition:	1st ed. 2006.
Series:	Theoretical Computer Science and General Issues ; 3940 Springer eBook Collection.
Subjects:	Algorithms. Mathematical statistics. Computers. Artificial intelligence. Optical data processing. Pattern recognition. Electronic resources (E-books)
Online Access:	Click to view e-book
Holy Cross Note:	Loaded electronically. Electronic access restricted to members of the Holy Cross Community.

Table of Contents:

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.

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