Embedding heterogeneous data by preserving multiple kernels

Heterogeneous data may arise in many real-life applications under different scenarios. In this paper, we formulate a general framework to address the problem of modeling heterogeneous data. Our main contribution is a novel embedding method, called multiple kernel preserving embedding (MKPE), which projects heterogeneous data into a unified embedding space by preserving crossdomain interactions and within-domain similarities simultaneously. These interactions and similarities between data points are approximated with Gaussian kernels to transfer local neighborhood information to the projected subspace. We also extend our method for out-of-sample embedding using a parametric formulation in the projection step. The performance of MKPE is illustrated on two tasks: (i) modeling biological interaction networks and (ii) cross-domain information retrieval. Empirical results of these two tasks validate the predictive performance of our algorithm.