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Research on High-Dimensional Data Reduction
Cuihua Tian,Yan Wang,Xueqin Lin,Jing Lin,Jiangshui Hong 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.1
In this paper, features of high-dimensional data are analyzed, and existing problems of the Canonical Correlation Analysis (CCA) are analyzed for a single view of a full supervised view data. In order to improve CCA, we introduce the method of classifier and present a Classifying to Reduce Correlation Dimensionality (CRCD). Meanwhile, combining big interval learning method, we propose the big correlation analysis (BCA). At last, experiments are respectively conducted by using artificial data set and UCI standard data set. The result shows that methods are feasible and effective.