Local Maximal Marginal Embedding with Application to Face Recognition

Many problems in information processing involve some form of dimensionality reduction. This paper develops a new approach for dimensionality reduction of high dimensional data, called local maximal marginal (interclass) embedding (LMME), to manifold learning and pattern recognition. LMME can be seen as a linear approach of a multimanifolds-based learning framework which integrates the information of neighbor and class relations. LMME characterize the local maximal marginal scatter as well as the local intraclass compactness, seeking to find a projection that maximizes the local maximal margin and minimizes the local intraclass scatter. This characteristic makes LMME more powerful than the most up-to-data method, Marginal Fisher Analysis (MFA), and maintain all the advantages of MFA. The proposed algorithm is applied to face recognition and is examined using the Yale, AR, ORL and face image databases. The experimental results show LMME consistently outperforms PCA, LDA and MFA, owing to the locally discriminating nature. This demonstrates that LMME is an effective method for face recognition.