Cross-modal similarity learning via pairs, preferences, and active supervision
© 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. We present a probabilistic framework for learning pairwise similarities between objects belonging to different modalities, such as drugs and proteins, or text and images. Our framework is based on learning a binary code based representation for objects in each modality, and has the following key properties: (i) it can leverage both pairwise as well as easy-to-obtain relative preference based cross-modal constraints, (ii) the probabilistic framework naturally allows querying for the most useful/informative constraints, facilitating an active learning setting (existing methods for cross-modal similarity learning do not have such a mechanism), and (iii) the binary code length is learned from the data. We demonstrate the effectiveness of the proposed approach on two problems that require computing pairwise similarities between cross-modal object pairs: cross-modal link prediction in bipartite graphs, and hashing based cross-modal similarity search.
Zhen, Y; Rai, P; Zha, H; Carin, L
Proceedings of the National Conference on Artificial Intelligence
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International Standard Book Number 13 (ISBN-13)