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Subspace, latent structure and feature selection : Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005 ; revised selected papers / Craig Saunders [and others] (eds.).

By: SLSFS 2005 (2005 : Bohinj, Slovenia)Contributor(s): Saunders, CraigMaterial type: TextTextSeries: Serienbezeichnung | Lecture notes in computer science ; 3940.Publisher: Berlin ; New York : Springer, ©2006Description: 1 online resource (x, 208 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9783540341383; 3540341382; 3540341374; 9783540341376Other title: Statistical and Optimization Perspectives Workshop | SLSFS 2005Subject(s): System identification -- Congresses | Latent structure analysis -- Congresses | Computer vision -- Congresses | Analyse de structure latente -- Congrès | Identification des systèmes -- Congrès | Vision par ordinateur -- Congrès | SCIENCE -- System Theory | TECHNOLOGY & ENGINEERING -- Operations Research | Latent structure analysis | Computer vision | Analyse de structure latente | Systèmes, Identification des | Vision par ordinateur | System identification | Informatique | Computer vision | Latent structure analysis | System identification | algoritmen | algorithms | computeranalyse | computer analysis | beeldverwerking | image processing | machine vision | waarschijnlijkheid | probability | statistiek | statistics | computerwetenschappen | computer sciences | kunstmatige intelligentie | artificial intelligence | computational science | patroonherkenning | pattern recognition | Information and Communication Technology (General) | Informatie- en communicatietechnologie (algemeen)Genre/Form: Electronic books. | Conference papers and proceedings. Additional physical formats: Print version:: Subspace, latent structure and feature selection.DDC classification: 003/.1 LOC classification: QA402 | .S596 2005ebOther classification: N945. 14-532 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.
Action note: digitized 2010 committed to preserveSummary: "Many of the papers in this proceedings volume were presented at the PASCAL Workshop entitled Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimization Perspectives which took place in Bohinj, Slovenia during February, 23-25 2005."
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Includes bibliographical references and author index.

"Many of the papers in this proceedings volume were presented at the PASCAL Workshop entitled Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimization Perspectives which took place in Bohinj, Slovenia during February, 23-25 2005."

Print version record.

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Electronic reproduction. [Place of publication not identified] : HathiTrust Digital Library, 2010. MiAaHDL

Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. MiAaHDL

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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.

English.

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