Lu, Fangfang; Li, Hongdong
In this paper, we present an iterative approach to Fisher discriminant analysis called Kullback-Leibler discriminant analysis (KLDA) for both linear and nonlinear feature extraction. We pose the conventional problem of discriminative feature extraction into the setting of function optimization and recover the feature transformation matrix via maximization of the objective function. The proposed objective function is defined by pairwise distances between all pairs of classes and the...[Show more]
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