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Heterogeneous data alignment for cross-media computing

Massive data sets are generated everyday, and knowledge spreading trends to involve diverse media types and information sources. The new situation emerges some new challenging research problems: (1) How to bridge the heterogeneity and mine the shared information among cross-view representations? (2) How to build the semantic association among heterogeneous information objects. (3) How to fully explore the complementary information underlying heterogeneous information objects to cooperatively make decision. In fact, the core of these problems is to align heterogeneous data. In this paper, we first give a short introduction about heterogeneous data alignment. And then, two ongoing works in our group about heterogeneous data alignment are discussed, i.e., consistent pattern mining and modality-dependent cross-media retrieval. Finally, we conclude this paper and discuss some potential applications of heterogeneous data alignment.