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A New Optimal Classifier Architecture to Aviod the Dimensionality Curse

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Pattern Recognition and Image Analysis (IbPRIA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2652))

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Abstract

In paper we present the theoretical foundation for optimal classification using class-specific features and provide examples of its use. A new PDF projection theorem makes it possible to project probability density functions from a low-dimensional feature space back to the raw data space. An M-ary classifier is constructed by estimating the PDFs of class-specific features, then transforming each PDF back to the raw data space where they can be fairly compared. Although statistical sufficiency is not a requirement, the classifier thus constructed will become equivalent to the optimal Bayes classifier if the features meet sufficiency requirements individually for each class. This classifier is completely modular and avoids the dimensionality curse associated with large complex problems. By recursive application of the projection theorem, it is possible to analyze complex signal processing chains. It is possible to automate the feature and model selection process by direct comparison of log-likelihood values on the common raw data domain. Pre-tested modules are available for a wide range of features including linear functions of independent random variables, cepstrum, and MEL cepstrum.

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Author information

Authors and Affiliations

  1. Naval Undersea Warfare Center, Newport, RI, 02841, USA

    Paul M. Baggenstoss

Authors
  1. Paul M. Baggenstoss
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Editor information

Editors and Affiliations

  1. Unitat de Gràfics i Visió per Ordinador Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears Edifici Anselm Turmeda, Ctra. de Valldemossa km 7,5, 07122, Palma de Mallorca, Spain

    Francisco José Perales

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio J. C. Campilho

  3. Departamento de Ciencias da la Computacíon e I.A., Universidad de Granada, E.T. S. Ing. Informática, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  4. Dept. System Engineering and Automation, Universitat Politècnica de Catalunya (UPC) Barcelona, Spain

    Alberto Sanfeliu

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© 2003 Springer-Verlag Berlin Heidelberg

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Baggenstoss, P.M. (2003). A New Optimal Classifier Architecture to Aviod the Dimensionality Curse. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_9

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  • DOI: https://doi.org/10.1007/978-3-540-44871-6_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40217-6

  • Online ISBN: 978-3-540-44871-6

  • eBook Packages: Springer Book Archive

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