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Feature informativeness in high-dimensional discriminant analysis
Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
Responsible organisation
2003 (English)In: Communications in Statistics: Theory and Methods, ISSN 0361-0926, Vol. 32, no 2, 459-474 p.Article in journal (Refereed) Published
Abstract [en]

A concept of feature informativeness was introduced as a way of measuring the discriminating power of a set of features. A question of interest is how this property of features affects the discrimination performance. The effect is assessed by means of a weighted discriminant function, which distributes weights among features according to their informativeness. The asymptotic normality of the weighted discriminant function is proven and the limiting expressions for the errors are obtained in the growing dimension asymptotic framework, i.e., when the number of features is proportional to the sample size. This makes it possible to establish the optimal in a sense of minimum error probability type of weighting.

Place, publisher, year, edition, pages
2003. Vol. 32, no 2, 459-474 p.
Keyword [en]
limiting error probability
National Category
Mathematics
Identifiers
URN: urn:nbn:se:miun:diva-2268DOI: 10.1081/STA-120018195ISI: 000181233900010Scopus ID: 2-s2.0-0037292810Local ID: 1494OAI: oai:DiVA.org:miun-2268DiVA: diva2:27300
Available from: 2008-09-30 Created: 2008-09-30 Last updated: 2016-10-21Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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