Subspace, Latent Structure and Feature Selection

Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers

224 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

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.

• Reihe: Lecture Notes in Computer Science
• Themen: Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI) Mustererkennung
• ISBN: 9783540341376
• Sprache: Englisch
• Erscheinungsdatum: 16.05.2006
• Größe: 235 x 155 mm
• Verlag: Springer
• Herausgegeben von: Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor