This abstract proposes to automatically estimate the optimal regularization parameter of SRDA based on the perturbation linear discriminant analysis (PLDA). In addition, two parameter estimation methods for the kernel version of SRDA are also developed. One is derived from the method of optimal regularization parameter estimation for SRDA. The other is to utilize the kernel version of PLDA. Experiments on a number of publicly available databases demonstrate the effectiveness of the proposed methods for face recognition, spoken letter recognition, handwritten digit recognition, and text categorization.
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