Enhancing Clustering Quality through Landmark-Based Dimensionality Reduction
ACM Transactions on Knowledge Discovery from Data (TKDD)
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Pattern Recognition
A two-stage linear discriminant analysis for face-recognition
Pattern Recognition Letters
A feature selection method using improved regularized linear discriminant analysis
Machine Vision and Applications
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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 overlapping between the classes is minimum for the projected feature vectors in the reduced feature space. This overlapping, 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 overlapping 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.