CAJ | 학술논문

主成分分析是一种常用的特征选择算法,经典方法是计算各个特征之间的相关,但是相关无法评估变量间的非线性关系.互信息可用于衡量两个变量间相互依赖的强弱程度,且不局限于线性相关,鉴于此,提出一种基于互信息的主成分分析特征选择算法.该算法计算特征间的互信息,以互信息矩阵的特征值作为评价准则确定主成分的个数,并衡量主成分分析特征选择的效果.通过实例对所提出方法和传统主成分分析方法进行比较,并以神经网络为分类器分析分类效果.
주성분분석시일충상용적특정선택산법,경전방법시계산각개특정지간적상관,단시상관무법평고변량간적비선성관계.호신식가용우형량량개변량간상호의뢰적강약정도,차불국한우선성상관,감우차,제출일충기우호신식적주성분분석특정선택산법.해산법계산특정간적호신식,이호신식구진적특정치작위평개준칙학정주성분적개수,병형량주성분분석특정선택적효과.통과실례대소제출방법화전통주성분분석방법진행비교,병이신경망락위분류기분석분류효과.
@@@@Principal component analysis(PCA) is a common method for feature selection. The classical procedure to obtain principal components is calculating the correlation matrix between features. However, the correlation cannot reflect the nonlinear relationship. Mutual information measures the interdependence strength between variables which are not limited to the linear correlation. PCA based on mutual information(MIPCA) for feature selection is presented. The algorithm calculates the mutual information matrix and extracts the eigenvalues as the criteria to determine the number of principal components and assess the effect of feature selection. Finally, the proposed algorithm is compared with PCA by cases, and the efficiency of classification is tested by neuron network.