Learning with Heterogenous Data Sets by Weighted Multiple Kernel Canonical Correlation Analysis

A new formulation of weighted multiple kernel based canonical correlation analysis(WMKCCA) is proposed in this paper. Computational issues are also considered in the proposed method to make it feasible on large data sets. This method uses incomplete Cholesky decomposition(ICD) and singular value decomposition(S VD) to approximate the original eigenvalue problem for low rank. For the weighted extension, an incremental eigenvalue decomposition method is proposed to avoid recalculating eigenvalue each time weights are changed. Based on WMKCCA we proposed, a machine learning framework to extract common information among heterogeneous data sets is purposed and experimental results on two UCI data sets are reported.