Adaptive weighted learning for linear regression problems via KullbackLeibler divergence
In this paper, we propose adaptive weighted learning for linear regression problems via the KullbackLeibler (KL) divergence. The alternative optimization method is used to solve the proposed model. Meanwhile, we theoretically demonstrate that the solution of the optimization algorithm converges to a stationary point of the model. In addition, we also fuse global linear regression and class-oriented linear regression and discuss the problem of parameter selection. Experimental results on face and handwritten numerical character databases show that the proposed method is effective for image classification, particularly for the case that the samples in the training and testing set have different characteristics.
(kl) kullbackleibler (glr) global linear regression (colr) class-oriented linear regression (wglr) weighted global linear regression (wcolr) weighed class-oriented linear regression (flr) the fusion of glr and colr (wflr) the fusion of wglr and wcolr (nn) nearest neighbor (rlr) robust linear regression (mc) maximum correntropy (src) sparse representation classification