Subspace, Latent Structure and Feature Selection

Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers

  • Craig Saunders
  • Marko Grobelnik
  • Steve Gunn
  • John Shawe-Taylor
Conference proceedings

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

Table of contents

  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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About these proceedings

Keywords

3D Bayesian inference STATISTICA algorithm algorithmic learning algorithms calculus image reconstruction learning machine learning optimisation methods optimization statistical analysis statistical learning statistical modeling

Editors and affiliations

  • Craig Saunders
    • 1
  • Marko Grobelnik
    • 2
  • Steve Gunn
    • 3
  • John Shawe-Taylor
    • 4
  1. 1.ISIS Research GroupUniversity of SouthamptonU.K.
  2. 2.Dept. of Knowledge TechnologiesJozef Stefan InstituteLjubljanaSlovenia
  3. 3.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  4. 4.The Centre for Computational Statistics and Machine Learning Department of Computer ScienceUniversity College LondonLondonUK

Bibliographic information

  • DOI https://doi.org/10.1007/11752790
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-34137-6
  • Online ISBN 978-3-540-34138-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book