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Subspace, Latent Structure and Feature Selection [electronic resource] :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.

by Saunders, Craig [editor.]; Grobelnik, Marko [editor.]; Gunn, Steve [editor.]; Shawe-Taylor, John [editor.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 3940Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.Description: X, 209 p. Also available online. online resource.ISBN: 9783540341383.Subject(s): Computer science | Computer software | Artificial intelligence | Computer vision | Optical pattern recognition | Computer Science | Algorithm Analysis and Problem Complexity | Probability and Statistics in Computer Science | Computation by Abstract Devices | Artificial Intelligence (incl. Robotics) | Image Processing and Computer Vision | Pattern RecognitionDDC classification: 005.1 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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, held in Bohinj, Slovenia in February 2005. The 9 revised full papers presented together with 5 invited papers were carefully selected during two rounds of reviewing and improvement for inclusion in the book. The papers reflect the key approaches that have been developed for subspace identification and feature selection using dimension reduction techniques, subspace methods, random projection methods, statistical analysis methods, Bayesian approaches to feature selection, latent structure analysis/probabilistic LSA, and optimisation methods.
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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, held in Bohinj, Slovenia in February 2005. The 9 revised full papers presented together with 5 invited papers were carefully selected during two rounds of reviewing and improvement for inclusion in the book. The papers reflect the key approaches that have been developed for subspace identification and feature selection using dimension reduction techniques, subspace methods, random projection methods, statistical analysis methods, Bayesian approaches to feature selection, latent structure analysis/probabilistic LSA, and optimisation methods.

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