The Relationship Between Canonical Correlation Analysis and Minimum Squared Error Classifier

摘要：

Canonical Correlation Analysis (CCA) has recently attracted great attention and many experimental results have illustrated its effectiveness.In this paper,we study the relationship between CCA classifier and minimum squared error (MSE) classifier.It helps us look into the nature of CCA classifier.In traditional CCA method,the class-membership matrix is deliberately coded in full rank.Under this case,we will prove CCA is equivalent to MSE classifier.It is also shown that even the class-membership matrix is centered and thus not in full rank,CCA is equivalent to Fisher Linear Discriminant Analysis (FDA).Some experiments are presented to verify the results.

作者: Guibin Yang Hongbin Zhang

作者单位: Computer Institute of Beijing University of Technology,Beijing China 100124

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）