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Class-Specific Subspace Discriminant Analysis for High-Dimensional Data

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Subspace, Latent Structure and Feature Selection (SLSFS 2005)

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

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Abstract

We propose a new method for discriminant analysis, called High Dimensional Discriminant Analysis (HDDA). Our approach is based on the assumption that high dimensional data live in different subspaces with low dimensionality. We therefore propose a new parameterization of the Gaussian model to classify high-dimensional data. This parameterization takes into account the specific subspace and the intrinsic dimension of each class to limit the number of parameters to estimate. HDDA is applied to recognize object parts in real images and its performance is compared to classical methods.

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Author information

Authors and Affiliations

  1. LMC – IMAG, Université Grenoble 1, BP 53, 38041 Cedex 9, Grenoble, France

    Charles Bouveyron & Stéphane Girard

  2. LEAR – INRIA Rhône-Alpes, 655 avenue de l’Europe, Montbonnot, 38334 Cedex, Saint-Ismier, France

    Charles Bouveyron & Cordelia Schmid

Authors
  1. Charles Bouveyron
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  2. Stéphane Girard
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  3. Cordelia Schmid
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Editor information

Editors and Affiliations

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

    Craig Saunders

  2. Dept. of Knowledge Technologies, Jozef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Marko Grobelnik

  3. School of Electronics and Computer Science, University of Southampton, Building 1, Highfield Campus, SO17 1BJ, Southampton, UK

    Steve Gunn

  4. The Centre for Computational Statistics and Machine Learning Department of Computer Science, University College London, Gower St., WC1E 6BT, London, UK

    John Shawe-Taylor

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© 2006 Springer-Verlag Berlin Heidelberg

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Bouveyron, C., Girard, S., Schmid, C. (2006). Class-Specific Subspace Discriminant Analysis for High-Dimensional Data. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds) Subspace, Latent Structure and Feature Selection. SLSFS 2005. Lecture Notes in Computer Science, vol 3940. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752790_9

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  • DOI: https://doi.org/10.1007/11752790_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34137-6

  • Online ISBN: 978-3-540-34138-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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