Nonparametric Feature Extraction via Direct Maximum Margin Alignment

As an important step in machine learning and information processing, feature extraction has widely received attention in the past decades. For high-dimensional data, feature extraction problem usually comes down to exploit the intrinsic pattern information with dimensionality reduction. Since nonparametric approaches are applicable without parameter installation, they are more preferred in many real-world applications. In this work, a novel approach to direct maximum margin alignment (DMMA), is proposed for nonparametric feature reduction and extraction. Though there has been the straightforward solution for the discriminative ratio based subspace selection, such type of solution is still unavailable for maximum margin alignment. In terms of the kernel-view idea, DMMA can be performed by bringing in sample kernel, while the computational efficiency is achievable. Experiments on pattern recognition show that the proposed method is able to obtain comparable performance with several state-of-the-art algorithms.