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Semi-supervised feature selection based on structure and constraints preserving


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Semi-supervised feature selection based on structure and constraints preserving
Pan Jun1Wang Ruiqin2Kong Fansheng3
1.Institute of Information Security; 2.College of Physics and Electronic Information Engineering, Wenzhou University,Wenzhou 325035,China; 3.College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China
feature selection semi-supervised learning pairwise constrains structure and constraints preserving feature ranking geometrical structure supervision information
To overcome the deficiency of most existing feature selection methods which fairly respect both the geometrical structure and the supervision information,a novel approach called semi-supervised feature selection based on structure and constraints preserving is proposed.In this method,both the pairwise constraints and the local and nonlocal structure are taken into account,and a new feature selection criterion,i.e.structure and constraints preserving(SCP)score is defined.The SCP score exploites abundant unlabeled data points to learn the geometrical structure of the data space,and uses a few pairwise constraints to discover the margins of different classes.Those features that can preserve the geometrical structure and pairwise constraints information are selected.Experimental results from several datasets show that the proposed method achieves better performance than the feature ranking selection methods.


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Last Update: 2014-08-31