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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

  • Conference proceedings
  • © 2006

Overview

Editors:
  1. Craig Saunders

    1. ISIS Research Group, University of Southampton, U.K.

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  2. Marko Grobelnik

    1. Dept. of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia

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  3. Steve Gunn

    1. School of Electronics and Computer Science, University of Southampton, Southampton, UK

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  4. John Shawe-Taylor

    1. The Centre for Computational Statistics and Machine Learning Department of Computer Science, University College London, London, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3940)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: SLSFS 2005.

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Table of contents (14 papers)

  1. Front Matter

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  2. Invited Contributions

    1. Discrete Component Analysis

      • Wray Buntine, Aleks Jakulin
      Pages 1-33

    2. Overview and Recent Advances in Partial Least Squares

      • Roman Rosipal, Nicole Krämer
      Pages 34-51

    3. Random Projection, Margins, Kernels, and Feature-Selection

      • Avrim Blum
      Pages 52-68

    4. Some Aspects of Latent Structure Analysis

      • D. M. Titterington
      Pages 69-83

    5. Feature Selection for Dimensionality Reduction

      • Dunja Mladenić
      Pages 84-102

  3. Contributed Papers

    1. Auxiliary Variational Information Maximization for Dimensionality Reduction

      • Felix Agakov, David Barber
      Pages 103-114

    2. Constructing Visual Models with a Latent Space Approach

      • Florent Monay, Pedro Quelhas, Daniel Gatica-Perez, Jean-Marc Odobez
      Pages 115-126

    3. Is Feature Selection Still Necessary?

      • Amir Navot, Ran Gilad-Bachrach, Yiftah Navot, Naftali Tishby
      Pages 127-138

    4. Class-Specific Subspace Discriminant Analysis for High-Dimensional Data

      • Charles Bouveyron, Stéphane Girard, Cordelia Schmid
      Pages 139-150

    5. Incorporating Constraints and Prior Knowledge into Factorization Algorithms – An Application to 3D Recovery

      • Amit Gruber, Yair Weiss
      Pages 151-162

    6. A Simple Feature Extraction for High Dimensional Image Representations

      • Christian Savu-Krohn, Peter Auer
      Pages 163-172

    7. Identifying Feature Relevance Using a Random Forest

      • Jeremy Rogers, Steve Gunn
      Pages 173-184

    8. Generalization Bounds for Subspace Selection and Hyperbolic PCA

      • Andreas Maurer
      Pages 185-197

    9. Less Biased Measurement of Feature Selection Benefits

      • Juha Reunanen
      Pages 198-208

  4. Back Matter

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  1. Subspace, Latent Structure and Feature Selection

Keywords

Editors and Affiliations

  • ISIS Research Group, University of Southampton, U.K.

    Craig Saunders

  • Dept. of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia

    Marko Grobelnik

  • School of Electronics and Computer Science, University of Southampton, Southampton, UK

    Steve Gunn

  • The Centre for Computational Statistics and Machine Learning Department of Computer Science, University College London, London, UK

    John Shawe-Taylor

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