Maximum Class Boundary Criterion For Supervised Dimensionality Reduction

Participation of class-wise noisy patterns may mislead the selection process of relevant patterns for subspace projection. And modelling between-class scatter for each class using the patterns that are nearer to the corresponding class decision boundary may improve the quality of feature generation. In this manuscript, a novel dimensionality reduction method, named Maximum Class Boundary Criterion (MCBC) is proposed. MCBC increases class separability by realizing the significant class-boundary and class-non-boundary patterns after the elimination of noisy patterns. The objective of MCBC is modeled such that the class-boundary patterns are pushed away from the corresponding class means and class-non-boundary patterns are forced towards their class means. As a result, the classification performance of the extracted MCBC features is improved. Experimental study is performed on UCI machine learning and face recognition data to highlight the performance of MCBC. The results conclude that MCBC can generate better discriminative features compared to the state-of-the-art dimensionality reduction methods.