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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

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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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Keywords

Table of contents (14 papers)

  1. Front Matter

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  3. Contributed Papers

    1. Auxiliary Variational Information Maximization for Dimensionality Reduction

      • Felix Agakov, David Barber
      Pages 103-114
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    2. Constructing Visual Models with a Latent Space Approach

      • Florent Monay, Pedro Quelhas, Daniel Gatica-Perez, Jean-Marc Odobez
      Pages 115-126
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    3. Is Feature Selection Still Necessary?

      • Amir Navot, Ran Gilad-Bachrach, Yiftah Navot, Naftali Tishby
      Pages 127-138
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    4. Class-Specific Subspace Discriminant Analysis for High-Dimensional Data

      • Charles Bouveyron, Stéphane Girard, Cordelia Schmid
      Pages 139-150
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    5. Incorporating Constraints and Prior Knowledge into Factorization Algorithms – An Application to 3D Recovery

      • Amit Gruber, Yair Weiss
      Pages 151-162
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    6. A Simple Feature Extraction for High Dimensional Image Representations

      • Christian Savu-Krohn, Peter Auer
      Pages 163-172
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    7. Identifying Feature Relevance Using a Random Forest

      • Jeremy Rogers, Steve Gunn
      Pages 173-184
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    8. Generalization Bounds for Subspace Selection and Hyperbolic PCA

      • Andreas Maurer
      Pages 185-197
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    9. Less Biased Measurement of Feature Selection Benefits

      • Juha Reunanen
      Pages 198-208
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  4. Back Matter

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Other volumes

  1. Subspace, Latent Structure and Feature Selection

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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