A new construction method of neighbor graph based on correlative columns information for marginal fisher analysis

Marginal fisher analysis is a typical supervised method which has been used in many practical problems such as face recognition. However, MFA mainly depends on its essential neighbor graphs-intrinsic graph and penalty graph. Intrinsic graph characterizes the intra-class compactness while the inter-class graph characterizes the inter-class separability. Consequently, neighbor graph construction plays a vital role on the performance of MFA. In this paper, we propose a new construction method of intrinsic graph and penalty graph for marginal fisher analysis. It is based on correlative columns information, so we name this new method as Correlative Columns Information based MFA (CCIMFA). CCIMFA can well show the spatial structure information of the original image matrices, and also can preserve the corresponding columns information. CCIMFA also has anther attractive property that is columns' noise immunity. In order to test and evaluate CCIMFA's performance, a series of experiments were performed on the well-known face databases: ORL and Yale face databases. The experimental results show that CCIMFA achieves better performance than MFA. © 2013 Binary Information Press.