CAJ | 학술논문

针对基于图嵌入的鉴别投影方法对近邻参数的敏感以及实际应用中样本类别信息不足对图嵌入方法鉴别性能的影响，提出一种基于自适应近邻选择和低秩表示的半监督鉴别分析方法。该方法利用所有类内样本点构造类内图来描述类内样本的紧致性，借助最远类内样本的邻域自适应地选取该邻域内不同类样本点构造类间图，以描述类间样本的可分性；此外，利用低秩表示方法挖掘不带类别信息样本的潜在低秩结构，以保留样本的全局相似关系。在ORL和FERET人脸数据库上的实验结果，验证了文中方法的有效性及对噪声的鲁棒性。
침대기우도감입적감별투영방법대근린삼수적민감이급실제응용중양본유별신식불족대도감입방법감별성능적영향，제출일충기우자괄응근린선택화저질표시적반감독감별분석방법。해방법이용소유류내양본점구조류내도래묘술류내양본적긴치성，차조최원류내양본적린역자괄응지선취해린역내불동류양본점구조류간도，이묘술류간양본적가분성；차외，이용저질표시방법알굴불대유별신식양본적잠재저질결구，이보류양본적전국상사관계。재ORL화FERET인검수거고상적실험결과，험증료문중방법적유효성급대조성적로봉성。
Considering the discriminant projection methods based on graph embedding are sensitive to the neighbor parameter and the fact that there is no sufficient class-label information of samples in practical applica-tions which has an impact on the performance of graph embedding based methods, a semi-supervised discrimi-nant analysis method based on adaptive neighbor selection and low-rank representation is proposed. The method uses all the intraclass samples to construct the intraclass graph which can characterize the intraclass compactness, and simultaneously adaptively selects the interclass samples within the neighborhood produced by the farthest in-traclass sample to construct the interclass graph which is used to characterize the interclass separability. Further-more, the low-rank representation approach is applied to mine the latent low-rank structure of unlabeled samples and thus preserve the global similarity relationship of samples. Experimental results on ORL and FERET face da-tabases demonstrate the effectiveness of our method and the robustness to noise.