Rotational linear discriminant analysis technique for dimensionality reduction

Sharma, Alokanand (2008) Rotational linear discriminant analysis technique for dimensionality reduction. IEEE Transactions on Knowledge and Data Engineering, 20 (10). pp. 1336-1347. ISSN 1041-4347

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Abstract

The linear discriminant analysis (LDA) technique is very popular in pattern recognition for dimensionality reduction. It is a supervised learning technique that finds a linear transformation such that the overlap between the classes is minimum for the projected feature vectors in the reduced feature space. This overlap, if present, adversely affects the classification performance. In this paper, we introduce prior to dimensionality-reduction transformation an additional rotational transform that rotates the feature vectors in the original feature space around their respective class centroids in such a way that the overlap between the classes in the reduced feature space is further minimized. As a result, the classification performance significantly improves, which is demonstrated using several data corpuses.

Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User:	Users 24 not found.
Date Deposited:	07 Feb 2008 22:29
Last Modified:	18 Jul 2012 01:58
URI:	https://repository.usp.ac.fj/id/eprint/4375

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