A hybrid feature extraction framework based on risk minimization and independence maximization

This paper presents a hybrid feature extraction framework based on two diverse optimization problems in aspects of risk and independence to extract features for higher classification performance. The risk minimization as a supervised approach pursues maximum generalization capability among data to directly improve classification performance, whereas the independence maximization process as an unsupervised method projects data onto a space which satisfies maximum independence to indirectly achieve better classification accuracy. Due to the direct and indirect relationship of risk minimization and independence maximization toward classification accuracy improvement, it is expected that features from the hybrid framework simultaneously satisfying both risk and independence criteria would result in the classification performance better than using either criterion. Experimental results show that the proposed hybrid framework provides higher classification performance than various existing feature extractors.