(OKKC) algorithm to combine multiple data sources. The objective of
-means clustering is formulated as a Rayleigh quotient function of the between-cluster scatter and the cluster membership matrix. To incorporate multiple data sources, the between-cluster matrix is calculated in the high dimensional Hilbert space where the heterogeneous data sources can be easily combined as kernel matrices. The objective to optimize the kernel combination and the cluster memberships on unlabeled data is non-convex. To solve it, we apply an alternating minimization  method to optimize the cluster memberships and the kernel coefficients iteratively to convergence. When the cluster membership is given, we optimize the kernel coefficients as kernel Fisher Discriminant (KFD) and solve it as least squares support vector machine (LSSVM). The objectives of KFD and
-means are combined in a unified model thus the two components optimize towards the same objective, therefore, the proposed alternating algorithm converges locally.
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