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Rotational Linear Discriminant Analysis Technique for Dimensionality Reduction

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2 Author(s)
Alok Sharma ; Griffith University, Brisbane and University of the South Pacific, Fiji ; Kuldip K. Paliwal

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.

Published in:

IEEE Transactions on Knowledge and Data Engineering  (Volume:20 ,  Issue: 10 )