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

Saunders, Craig (EDT); Grobelnik, Marko (EDT); Gunn, Steve (EDT); Shawe-Taylor, John (EDT)

ISBN 10: 3540341374 / ISBN 13: 9783540341376

Editore: Springer, 2006

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Dati bibliografici

• Titolo: Subspace, Latent Structure And Feature Selection : Statistical And Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005 Revised Selected Papers
• Editore: Springer
• Data di pubblicazione: 2006
• Lingua: Inglese
• ISBN 10: 3540341374
• ISBN 13: 9783540341376
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

This book constitutes the thoroughly refereed post-proceedings of the PASCAL (pattern analysis, statistical modelling and computational learning) Statistical and Optimization Perspectives Workshop on Subspace, Latent Structure and Feature Selection techniques, SLSFS 2005. The 9 revised full papers presented together with 5 invited papers reflect the key approaches that have been developed for subspace identification and feature selection using dimension reduction techniques, subspace methods, random projection methods, among others.

Contenuti

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.