Two realizations of a general feature extraction framework

Abstract A general feature extraction framework is proposed as an extension of conventional linear discriminant analysis. Two nonlinear feature extraction algorithms based on this framework are investigated. The first is a kernel function feature extraction (KFFE) algorithm. A disturbance term is introduced to regularize the algorithm. Moreover, it is revealed that some existing nonlinear feature extraction algorithms are the special cases of this KFFE algorithm. The second feature extraction algorithm, mean–STD 1 –norm feature extraction algorithm, is also derived from the framework. Experiments based on both synthetic and real data are presented to demonstrate the performance of both feature extraction algorithms.

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