2012 AProbabilisticModelforMultimoda

• (Zhen & Yeung, 2012) ⇒ Yi Zhen, and Dit-Yan Yeung. (2012). “A Probabilistic Model for Multimodal Hash Function Learning.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339678

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+A+Probabilistic+Model+for+Multimodal+Hash+Function+Learning
• http://dl.acm.org/citation.cfm?id=2339530.2339678&preflayout=flat#citedby

Quotes

Author Keywords

• Binary latent factor models; hash function learning; information search and retrieval; metric learning; multimodal similarity search; probabilistic algorithms

Abstract

In recent years, both hashing-based similarity search and multimodal similarity search have aroused much research interest in the data mining and other communities. While hashing-based similarity search seeks to address the scalability issue, multimodal similarity search deals with applications in which data of multiple modalities are available. In this paper, our goal is to address both issues simultaneously. We propose a probabilistic model, called multimodal latent binary embedding (MLBE), to learn hash functions from multimodal data automatically. MLBE regards the binary latent factors as hash codes in a common Hamming space. Given data from multiple modaliti]]es, we devise an efficient algorithm for the learning of binary latent factors which corresponds to hash function learning. Experimental validation of MLBE has been conducted using both synthetic data and two realistic data sets. Experimental results show that MLBE compares favorably with two state-of-the-art models.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 AProbabilisticModelforMultimoda	Dit-Yan Yeung Yi Zhen			A Probabilistic Model for Multimodal Hash Function Learning				10.1145/2339530.2339678		2012