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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

Author: Craig Saunders
Publisher: Berlin ; New York : Springer, ©2006.
Series: Lecture notes in computer science, 3940.
Edition/Format:   Print book : Conference publication : EnglishView all editions and formats
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This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on  Read more...

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Genre/Form: Conference papers and proceedings
Congresses
Congrès
Additional Physical Format: Online version:
SLSFS 2005 (2005 : Bohinj, Slovenia).
Subspace, latent structure and feature selection.
Berlin ; New York : Springer, ©2006
(OCoLC)606432255
Online version:
SLSFS 2005 (2005 : Bohinj, Slovenia).
Subspace, latent structure and feature selection.
Berlin ; New York : Springer, ©2006
(OCoLC)607735583
Material Type: Conference publication, Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Craig Saunders
ISBN: 3540341374 9783540341376
OCLC Number: 69983528
Description: x, 208 pages : illustrations ; 24 cm.
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.
Series Title: Lecture notes in computer science, 3940.
Other Titles: Statistical and Optimization Perspectives Workshop
SLSFS 2005
Responsibility: Craig Saunders [and others] (eds.).

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