Subspace, Latent Structure and Feature Selection : Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers / / edited by Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor

• Pubblicazione: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006
• Edizione: 1st ed. 2006.
• Descrizione fisica: 1 online resource (X, 209 p.)
• Disciplina: 003/.1
• Soggetto topico: Algorithms; Computer science - Mathematics; Mathematical statistics; Computer science; Artificial intelligence; Computer vision; Pattern recognition systems; Probability and Statistics in Computer Science; Theory of Computation; Artificial Intelligence; Computer Vision; Automated Pattern Recognition
• Altri autori: SaundersCraig
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and author index.
• Nota di contenuto: 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.
• Sommario/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.
• Titolo autorizzato: Subspace, latent structure and feature selection
• ISBN: 3-540-34138-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910483873203321
• Lo trovi qui: Univ. Federico II
• Serie: Theoretical Computer Science and General Issues, . 2512-2029 ; ; 3940