CAJ | 학술논문

局部敏感鉴别分析（ LSDA）是一种基于向量学习的提取特征的算法，该算法使得属于同一类的相邻数据经投影后尽量靠近，但不同类的邻近数据则相远离。在实际应用中，由于小样本问题，通常先利用PCA算法对原始数据进行降维处理，然后再使用LSDA算法提取特征。然而，这种方法会丢掉一些重要的鉴别信息。提出了最大边距局部敏感鉴别分析（ MM-LSDA ）算法，直接从原始数据中提取特征，避免了鉴别信息的损失，同时使得同类中的近邻数据尽量靠近，而不同类之间的样本远离。在ORL和Yale人脸库上的仿真实验表明此算法更有效。
국부민감감별분석（ LSDA）시일충기우향량학습적제취특정적산법，해산법사득속우동일류적상린수거경투영후진량고근，단불동류적린근수거칙상원리。재실제응용중，유우소양본문제，통상선이용PCA산법대원시수거진행강유처리，연후재사용LSDA산법제취특정。연이，저충방법회주도일사중요적감별신식。제출료최대변거국부민감감별분석（ MM-LSDA ）산법，직접종원시수거중제취특정，피면료감별신식적손실，동시사득동류중적근린수거진량고근，이불동류지간적양본원리。재ORL화Yale인검고상적방진실험표명차산법경유효。
Locality Sensitive Discriminant Analysis ( LSDA ) was a liner manifold learning algorithm, it makes that the nearby data points with the same label are closed to each other while the nearby points with dif-ferent labels are far apart.In practice, because of small sample size problem, PCA is applied to reduce the di-mension of original data space before utilizing LSDA.However, this strategy may discard important discrimina-tive information.Maximum Margin Locality Sensitive Discriminant Analysis ( MM-LSDA) is proposed to over-come the above problem in this paper.The new method extracts features directly from original data, preserving the nearby data points with the same label, while aparting data points with different labels.Experiments on ORL and Yale databases show that the proposed method is more effective.