2015 NonTransitiveHashingwithLatentS

• (Ou et al., 2015) ⇒ Mingdong Ou, Peng Cui, Fei Wang, Jun Wang, and Wenwu Zhu. (2015). “Non-transitive Hashing with Latent Similarity Components.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783283

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+Non-transitive+Hashing+with+Latent+Similarity+Components
• http://dl.acm.org/citation.cfm?id=2783258.2783283&preflayout=flat#citedby

Quotes

Author Keywords

• Hashing; miscellaneous; non-transitive similarity; similarity components

Abstract

Approximating the semantic similarity between entities in the learned Hamming space is the key for supervised hashing techniques. The semantic similarities between entities are often non-transitive since they could share different latent similarity components. For example, in social networks, we connect with people for various reasons, such as sharing common interests, working in the same company, being alumni and so on. Obviously, these social connections are non-transitive if people are connected due to different reasons. However, existing supervised hashing methods treat the pairwise similarity relationships in a simple and unified way and project data into a single Hamming space, while neglecting that the non-transitive property cannot be ade - quately captured by a single Hamming space. In this paper, we propose a non-transitive hashing method, namely Multi-Component Hashing (MuCH), to identify the latent similarity components to cope with the non-transitive similarity relationships. MuCH generates multiple hash tables with each hash table corresponding to a similarity component, and preserves the non-transitive similarities in different hash table respectively. Moreover, we propose a similarity measure, called Multi-Component Similarity, aggregating Hamming similarities in multiple hash tables to capture the non-transitive property of semantic similarity. We conduct extensive experiments on one synthetic dataset and two public real-world datasets (i.e. DBLP and NUS-WIDE). The results clearly demonstrate that the proposed MuCH method significantly outperforms the state-of-art hashing methods especially on search efficiency.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 NonTransitiveHashingwithLatentS	Fei Wang Mingdong Ou Peng Cui Jun Wang Wenwu Zhu			Non-transitive Hashing with Latent Similarity Components				10.1145/2783258.2783283		2015