Type
ArticleKAUST Department
Computational Bioscience Research Center (CBRC)Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2014-09-07Preprint Posting Date
2014-09-27Online Publication Date
2014-09-07Print Publication Date
2015-01Permanent link to this record
http://hdl.handle.net/10754/556641
Metadata
Show full item recordAbstract
In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label. We argue that, with the learned classifier, the uncertainty of the true class label of a data sample should be reduced by knowing its classification response as much as possible. The reduced uncertainty is measured by the mutual information between the classification response and the true class label. To this end, when learning a linear classifier, we propose to maximize the mutual information between classification responses and true class labels of training samples, besides minimizing the classification error and reducing the classifier complexity. An objective function is constructed by modeling mutual information with entropy estimation, and it is optimized by a gradient descend method in an iterative algorithm. Experiments on two real world pattern classification problems show the significant improvements achieved by maximum mutual information regularization.Citation
Maximum mutual information regularized classification 2015, 37:1 Engineering Applications of Artificial IntelligencePublisher
Elsevier BVarXiv
1409.7780Additional Links
http://linkinghub.elsevier.com/retrieve/pii/S0952197614002085http://arxiv.org/abs/1409.7780
ae974a485f413a2113503eed53cd6c53
10.1016/j.engappai.2014.08.009