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Many applications are facing the problem of learning from an objective dataset, whereas information from other auxiliary sources may be beneficial but cannot be integrated into the objective dataset for learning. In this paper, the authors propose an omni-view learning approach to enable learning from multiple data collections. The theme is to organize heterogeneous data sources into a unified table with global data view. To achieve the omni-view learning goal, the authors consider that the objective dataset and the auxiliary datasets share some instance-level dependency structures.
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