CAJ | 학술논문

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。
희소표시분류중적자전선택지관중요，위료용교소적자전원자경호지표시원시훈련양본적국부신식，병차사학습출적자전경가구유판별신식，제출료일충기우국부보지준칙적희소표시자전학습방법。해방법장국부보지준칙강가재편마계수상，사득학습출적자전구유상근수거점적편마계수야보지근린관계적특성，종이보지원시훈련양본적국부신식。재확전YaleB、AR화COIL20수거고상적실험결과표명，문중방법적분류식별결과우우기타방법，설명해방법시유효적。
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.