Learning Multi-View Neighborhood Preserving Projections
نویسندگان
چکیده
•We address the problem of projecting data in different representations into a shared space, such that the Euclidean distance in this space provides a meaningful within-view as well as between-view similarity; •We formulate an objective function that expresses the intuitive concept that matching samples are mapped closely together in the output space, whereas non-matching samples are pushed apart; •We show that the resulting objective function can be efficiently optimized using the convex-concave procedure (CCCP); •Our proposed approach has a direct application for cross-media and content-based retrieval tasks.
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