Sparse Kernel Canonical Correlation Analysis
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چکیده
We review the recently proposed method of Relevance Vector Machines which is a supervised training method related to Support Vector Machines. We also review the statistical technique of Canonical Correlation Analysis and its implementation in a Feature Space. We show how the technique of Relevance Vectors may be applied to the method of Kernel Canonical Correlation Analysis to gain a very sparse representation of a data set and discuss why such a representation may be bene cial to an organism.
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تاریخ انتشار 2001