局部保持的稀疏表示字典学习

Dictionary Learning via Locality Preserving for Sparse Representation

稀疏表示分类中的字典选择至关重要,为了用较少的字典原子更好地表示原始训练样本的局部信息,并且使学习出的字典更加具有判别信息,提出了一种基于局部保持准则的稀疏表示字典学习方法.该方法将局部保持准则强加在编码系数上,使得学习出的字典具有相近数据点的编码系数也保持近邻关系的特性,从而保持原始训练样本的局部信息.在扩展YaleB、AR和COIL20数据库上的实验结果表明,文中方法的分类识别结果优于其他方法,说明该方法是有效的.

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 informa-tion 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.