Uncorrelated Multilinear Nearest Feature Line Analysis

In this paper, we propose a new subspace learning method, called uncorrelated multilinear nearest feature line analysis (UMNFLA), for the recognition of multidimensional objects, known as tensor objects. Motivated by the fact that existing nearest feature line (NFL) can effectively characterize the geometrical information of limited samples, and uncorrelated features are desirable for many pattern analysis applications since they contain minimum redundancy and ensure independence of features, we propose using the NFL metric to seek a feature subspace such that the within-class feature line (FL) distances are minimized and between-class FL distances are maximized simultaneously in the reduced subspace, and impose an uncorrelated constraint to extract statistically uncorrelated features directly from tensorial data. UMNFLA seeks a tensor-to-vector projection (TVP) that captures most of the variation in the original tensorial input, and employs sequential iterative steps based on the alternating projection method. Experimental results on the task of single trial electroencephalography (EEG) recognition suggest that UMN-FLA is particularly effective in determining the low-dimensional projection space needed in such recognition tasks.