应用主分量分析与粗糙集处理的特征提取

Using PCA and Rough Set for Feature Extraction

近年来,随着软计算理论的不断发展,粗糙集理论已经成为了目前研究的重点领域。论文讨论了主分量分析(PCA)与粗糙集的理论,并应用于图像特征提取中。采用PCA对输入向量进行甄别,应用粗糙集理论约简与分类无关或关系不大的向量。研究结果表明:在主成分分析中结合粗糙集理论可以排除无关向量的影响,并有效地进行特征提取。试验结果表明了结合两者能够提高模式分类的特征提取的效果。

With the recently developing of soft computing,Rough set theory,however,is being a hot research spot.In this paper,we discuss both Principle Component Analysis(PCA)and rough set theory,and apply them in feature extraction of image classification.We use PCA on selecting the input vector,and use rough set on reducing the inessential factors for classification.Our research indicate that using rough set and PCA together can exclude the unuseful factors and get a minimal set of vectors for image classification.The test results prove the better effects on using both them in pattern recognition than the method which only using PCA or rough set.