04314nam 22008415 450 991048387320332120251226203554.03-540-34138-210.1007/11752790(CKB)1000000000232970(SSID)ssj0000320257(PQKBManifestationID)11238875(PQKBTitleCode)TC0000320257(PQKBWorkID)10348180(PQKB)10370877(DE-He213)978-3-540-34138-3(MiAaPQ)EBC3068016(PPN)12313465X(BIP)34164062(BIP)13385102(EXLCZ)99100000000023297020100301d2006 u| 0engurnn|008mamaatxtccrSubspace, 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-Taylor1st ed. 2006.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2006.1 online resource (X, 209 p.) Theoretical Computer Science and General Issues,2512-2029 ;3940Bibliographic Level Mode of Issuance: Monograph3-540-34137-4 Includes bibliographical references and author index.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.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.Theoretical Computer Science and General Issues,2512-2029 ;3940AlgorithmsComputer scienceMathematicsMathematical statisticsComputer scienceArtificial intelligenceComputer visionPattern recognition systemsAlgorithmsProbability and Statistics in Computer ScienceTheory of ComputationArtificial IntelligenceComputer VisionAutomated Pattern RecognitionAlgorithms.Computer scienceMathematics.Mathematical statistics.Computer science.Artificial intelligence.Computer vision.Pattern recognition systems.Algorithms.Probability and Statistics in Computer Science.Theory of Computation.Artificial Intelligence.Computer Vision.Automated Pattern Recognition.003/.1Saunders Craig1752081MiAaPQMiAaPQMiAaPQBOOK9910483873203321Subspace, latent structure and feature selection4187295UNINA