CAJ | 학술논문

为解决多标签线性判别分析（MLDA）方法在非线性维数约简方面的局限性，提出了一种多标签核判别分析（MKDA）方法，并将其用于人脸的身份与性别识别中．该方法的基本思想是通过非线性映射将训练样本从输入空间映射到高维核特征空间中，并在该特征空间中进行基于MLDA的数据降维．在身份和性别识别中，首先采用MKDA方法对人脸图像特征向量进行降维，获取判别特征矢量集；其次，为每幅人脸图像赋予一个表征身份和性别的多标签类别矢量；最后，采用减秩回归模型（RRR）描述判别特征矢量与多标签类别矢量之间的回归关系，并利用该模型进行未知人脸的身份和性别识别．AR人脸数据库上的实验结果表明：在人脸身份和性别识别中，MKDA方法的识别率高于传统核判别分析（KDA）方法．
위해결다표첨선성판별분석（MLDA）방법재비선성유수약간방면적국한성，제출료일충다표첨핵판별분석（MKDA）방법，병장기용우인검적신빈여성별식별중．해방법적기본사상시통과비선성영사장훈련양본종수입공간영사도고유핵특정공간중，병재해특정공간중진행기우MLDA적수거강유．재신빈화성별식별중，수선채용MKDA방법대인검도상특정향량진행강유，획취판별특정시량집；기차，위매폭인검도상부여일개표정신빈화성별적다표첨유별시량；최후，채용감질회귀모형（RRR）묘술판별특정시량여다표첨유별시량지간적회귀관계，병이용해모형진행미지인검적신빈화성별식별．AR인검수거고상적실험결과표명：재인검신빈화성별식별중，MKDA방법적식별솔고우전통핵판별분석（KDA）방법．
A multi-label kernel discriminant analysis(MKDA)method is proposed to overcome the limitation of multi-label linear discriminant analysis (MLDA)on nonlinear dimensionality reduction, and applied to the recognition of face and gender.The basic idea of the MKDA method is to map the training data samples from the input data space to a high-dimensional kernel feature space via a non-linear mapping and then to perform data reduction based on the MLDA method in the feature space. During the recognition of face and gender,the dimensionality of the face image feature vectors is firstly reduced by using the MKDA method and a set of discriminative feature vector set is obtained. Then,a multi-label class vector indicating the class membership of face and gender is assigned to each face image.Finally,a reduced-rank regression (RRR)model is built to describe the relation-ship between the discriminative facial feature vectors and multi-label class vectors,and is applied to the face and gender recognition of an unknown face image.The experimental results on AR face da-tabase show that the recognition rates of the MKDA method are higher than those of the traditional kernel discriminant analysis (KDA)in face and gender recognition.