Adaptive weighted learning for linear regression problems via Kullback-Leibler divergence

Authors:
Zhizheng Liang;Youfu Li;Shixiong Xia
Affiliations:
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou City, China;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong;School of Computer Science and Technology, China University of Mining and Technology, Xuzhou City, China
Venue:
Pattern Recognition
Year:
2013

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Abstract

In this paper, we propose adaptive weighted learning for linear regression problems via the Kullback-Leibler (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.