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In this paper, we develop a new effective multiple kernel learning algorithm. First, we map the input data into m different feature spaces by m empirical kernels, where each generated feature space is taken as one view of the input space. Then, through borrowing the motivating argument from Canonical Correlation Analysis (CCA) that can maximally correlate the m views in the transformed coordinates,...
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