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Feature informativeness in high-dimensional discriminant analysis
Mid Sweden University .
2003 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, 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]
discriminant analysis, growing dimension asymptotic, feature informativeness, limiting error probability
National Category
URN: urn:nbn:se:kth:diva-86216DOI: 10.1081/STA-120018195ISI: 000181233900010OAI: diva2:500521
QC 20120816Available from: 2012-02-13 Created: 2012-02-13 Last updated: 2012-08-16Bibliographically approved

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Pavlenko, Tatjana
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Communications in Statistics - Theory and Methods

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ReferencesLink to record
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