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Unsupervised neural network learning procedures for feature extraction and classification

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Abstract

In this article, we review unsupervised neural network learning procedures which can be applied to the task of preprocessing raw data to extract useful features for subsequent classification. The learning algorithms reviewed here are grouped into three sections: information-preserving methods, density estimation methods, and feature extraction methods. Each of these major sections concludes with a discussion of successful applications of the methods to real-world problems.

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Authors and Affiliations

  1. Department of Psychology, McMaster University, L8S 4K1, Hamilton, Ont., Canada

    Suzanna Becker

  2. Department of Computer Science, King's College, London, Strand, WC2R 2LS, London, UK

    Mark Plumbley

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  1. Suzanna Becker
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The first author is supported by research grants from the James S. McDonnell Foundation (grant #93–95) and the Natural Sciences and Engineering Research Council of Canada. For part of this work, the second author was supported by a Temporary Lectureship from the Academic Initiative of the University of London, and by a grant (GR/J38987) from the Science and Engineering Research Council (SERC) of the UK.

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Becker, S., Plumbley, M. Unsupervised neural network learning procedures for feature extraction and classification. Appl Intell 6, 185–203 (1996). https://doi.org/10.1007/BF00126625

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