Subspace, Latent Structure and Feature Selection Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Saunders, Craig (Editor), Grobelnik, Marko (Editor), Gunn, Steve (Editor), Shawe-Taylor, John (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2006.
• Series: Lecture Notes in Computer Science, 3940
• Subjects: Computer science.; Computers.; Algorithms.; Mathematical statistics.; Artificial intelligence.; Image processing.; 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.

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.