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《Journal of South China University of Technology(Natural Science Edition)》 2014-01
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Dictionary Learning via Locality Preserving for Sparse Representation

Chen Si-bao;Zhao Ling;Luo Bin;School of Computer Science and Technology,Anhui University;Key Laboratory for Industrial Image Processing and Analysis of Anhui Province;  
The selection of dictionary is crucial to sparse representation classification. In order to preserve the local information of original training samples with less dictionary atoms and include more discriminant information in the learned dictionary,a new dictionary learning method based on the locality preserving criterion is proposed for sparse representation. In this method,the locality preserving criterion is imposed on coding coefficients,which makes the coding coefficients of neighboring data points in the dictionary close to each other and preserves the local information of original training samples. Experimental results on extended YaleB,AR and COIL20 databases show that the proposed method is effective because it is of higher classification performance than other methods.
【Fund】: 国家自然科学基金资助项目(61202228 61073116);; 高等学校博士学科点专项科研基金资助项目(20103401120005);; 安徽省高校自然科学研究重点项目(KJ2012A004)
【CateGory Index】: TP391.41
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