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

Craig Saunders

ISBN 10: 3540341374 / ISBN 13: 9783540341376

Editore: Springer, Springer, 2006

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9783540341376

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, Springer
• Data di pubblicazione: 2006
• Lingua: Inglese
• ISBN 10: 3540341374
• ISBN 13: 9783540341376
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 13 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 13 millimetri di profondità
• Peso: 347 grammi

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.