Optimal Regularization Parameter Estimation for Spectral Regression Discriminant Analysis
Wei J. Chen, Caifeng Shan, Geert de Haan · IEEE Transactions on Circuits and Systems for Video Technology · 2009
Spectral regression discriminant analysis (SRDA) is an efficient subspace learning method proposed recently. One important unsolved issue of SRDA is how to automatically determine an appropriate regularization parameter. In this letter, we present a method to estimate the optimal regularization parameter for SRDA. We test our method in different applications including head pose estimation, face recognition, and text categorization. Our extensive experiments evidently illustrate the effectiveness and efficiency of our approach.