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- W200097259 abstract "Discriminant analysis is usually carried out by projecting sample clusters in a multidimensional space onto a subspace of a lower dimension. To speak of the case of two distributions in the space R k , for example, the linear discriminant function c 'X (c, X being k-dimensional vectors) is considered, where the vector c is determined usually by maximizing the ratio of the external to the internal variance of c 'X. The quantity called the affinity serves well as a measure of information for discriminating distributions. The chapter presents a method for determining a projection for discrimination, that is, a method that minimizes the affinity of distributions concerned. The chapter also presents some properties of the affinity as a measure of information for discrimination and presents a method for determining a projection for efficient discrimination." @default.
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- W200097259 date "1973-01-01" @default.
- W200097259 modified "2023-09-25" @default.
- W200097259 title "DISCRIMINATION AND THE AFFINITY OF DISTRIBUTIONS" @default.
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- W200097259 doi "https://doi.org/10.1016/b978-0-12-154050-0.50018-6" @default.
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