Ridge-Penalty Regularization for Kernel-CCA
نویسندگان
چکیده
CCA and Kernel-CCA are powerful statistical tools that have been successfully employed for feature extraction. However, when working in high-dimensional signal spaces, care has to be taken to avoid overfitting. This paper discusses the influence of ridge penalty regularization on kernel-CCA by relating it to multivariate linear regression(MLR) and partial least squares(PLS). Experimental results of a pose estimation task will be given.
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تاریخ انتشار 2004