CAJ | 학술논문

特征选择是在损失较少信息的情况下处理高维图像数据的关键技术,是高维数据预处理的重要步骤.通过引入Fisher判别分析(Fisher Discriminant Analysis,FDA)和典型相关分析(Canonical Correlation Analysis,CCA)的思想,采用以样本的类标号形式给出的先验信息,考虑样本数据的局部性,提出了一种监督的基于Fisher判别信息的局部保持多投影向量分析方法(Locality Preserving Mult-projection Vector Fisher Discriminant Analysis,LPMVF).通过定义新准则,LPMVF具有以下优点:(1)便于计算,可有效避免奇异性;(2)借助标准核映射,可快速将LPMVF推广到非线性的特征空间;(3)与CCA算法类似,LPMVF最终得到一对投影变换,可有效嵌入样本数据,可将原始数据投影成一系列"有用的"特征形式,并使数据的投影在嵌入空间中更具可分离性;(4)与局部化的Fisher判别分析(Local Fisher Discriminant Analysis,简称LFDA)相比,LPMVF也能够有效保持数据样本间的局部近邻关系;(5)在大多数情况下,该文算法的学习能力甚至优于经典的FDA、KFD和LFDA算法.在几个标准数据集上的实验结果表明,LPMVF及其非线性的推广算法能够提取出描述能力更强的特征信息,可有效利用类标号监督信息提高分类性能.
특정선택시재손실교소신식적정황하처리고유도상수거적관건기술,시고유수거예처리적중요보취.통과인입Fisher판별분석(Fisher Discriminant Analysis,FDA)화전형상관분석(Canonical Correlation Analysis,CCA)적사상,채용이양본적류표호형식급출적선험신식,고필양본수거적국부성,제출료일충감독적기우Fisher판별신식적국부보지다투영향량분석방법(Locality Preserving Mult-projection Vector Fisher Discriminant Analysis,LPMVF).통과정의신준칙,LPMVF구유이하우점:(1)편우계산,가유효피면기이성;(2)차조표준핵영사,가쾌속장LPMVF추엄도비선성적특정공간;(3)여CCA산법유사,LPMVF최종득도일대투영변환,가유효감입양본수거,가장원시수거투영성일계렬"유용적"특정형식,병사수거적투영재감입공간중경구가분리성;(4)여국부화적Fisher판별분석(Local Fisher Discriminant Analysis,간칭LFDA)상비,LPMVF야능구유효보지수거양본간적국부근린관계;(5)재대다수정황하,해문산법적학습능력심지우우경전적FDA、KFD화LFDA산법.재궤개표준수거집상적실험결과표명,LPMVF급기비선성적추엄산법능구제취출묘술능력경강적특정신식,가유효이용류표호감독신식제고분류성능.
Feature selection has been an important preprocessing step in high-dimensional image data analysis without losing much intrinsic information.By introducing the ideas of Fisher Discriminant Analysis(FDA)and Canonical Correlation Analysis(CCA),the paper discusses the supervised feature selection problem where samples are accompanied with class labels and proposes a new Locality Preserving Multi-projection Vector Fisher Discriminant Analysis algorithm called LPMVF.LPMVF takes the local structure of the original data into account,so the multimodal samples data can be embedded appropriately.By defining the new guidelines.LPMVF hasthe following advantages:(1)LPMVF can be easily computed and can avoid the singular problems;(2)LPMVF can be easily extended to non-linear feature selection scenarios by employing the kernel trick;(3)Similar to CCA,LPMVF attempts to find two sets of basis vectors for two multivariate datasets of different classes,one for each class,which can project the original data onto a set of more useful features in the found embedding space, which would be benefit to classification and pattern recognition; (4) The same with Local Fisher Discriminant Analysis (LFDA) , LPMVF can preserve the local relationships between the data points; (5) In most cases, the learning performance of the LPMVF method is superior to those of the classical FDA, KFD and LFDA algorithms. The authors verify the feasibility and effectiveness of LPMVF by extensive visualization and classification tasks. Experimental results on the benchmark datasets show that LPMVF and its nonlinear extended algorithm can extract the good features and effectively improve the accuracy by introducing the class labels as priori knowledge.