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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 /

Contributor(s): 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 : Imprint: Springer, 2006.Description: X, 209 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540341383.Subject(s): Computer science | Computers | Algorithms | Mathematical statistics | Artificial intelligence | Image processing | 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 RecognitionOnline 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 eBooks
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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.

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