New approach to pattern recognition via comparison of maximum separations
Date
2011
Authors
Nechval, K.N.
Nechval, N.A.
Purgailis, M.
Strelchonok, V.F.
Berzins, G.
Moldovan, M.
Editors
Advisors
Journal Title
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Volume Title
Type:
Journal article
Citation
Computer Modelling and New Technologies, 2011; 15(2):30-40
Statement of Responsibility
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Abstract
Fisher’s linear discriminant analysis is a widely used multivariate statistical technique with two closely related goals:discrimination and classification. The technique is very popular among users of discriminant analysis. Some of the reasons for this are its simplicity and un-necessity of strict assumptions. In its original form, proposed by Fisher, the method assumes equality of population covariance matrices, but does not explicitly require multivariate normality. However, optimal classification performance of Fisher's discriminant function can only be expected when multivariate normality is present as well, since only good discrimination can ensure good allocation. In practice, we often are in need of analysing input data samples, which are not adequate for Fisher’s classification rule, such that the distributions of the groups are not multivariate normal or covariance matrices of thoseare different or there are strong multi-nonlinearities
This paper proposes a new approach to pattern recognition based on comparison of maximum separations in the input datasamples, where maximum separations are determined via Fisher’s separation criterion. The approach represents the improved pattern recognition procedure that allows one to take into account the cases which are not adequate for Fisher’s classification rule.Moreover, it allows one to classify sets of multivariate observations, where each of the sets contains more than one observation.For the cases, which are adequate for Fisher’s classification rule, the proposed approach gives the results similar to that of Fisher’s classification rule. Illustrative examples are given