Although hash function learning algorithms have achieved great success in recent years, most existing hash models are off-line, which are not suitable for processing sequential or online data. To address this problem, this paper proposes an online hash model to accommodate data coming in stream for online learning. Specifically, a new loss function is proposed to measure the similarity loss between a pair of data samples in hamming space. Then, a structured hash model is derived and optimized in a passive-aggressive way. Theoretical analysis on the upper bound of the cumulative loss for the proposed online hash model is provided. Furthermore, we extend our online hashing (OH) from a single model to a multimodel OH that trains multiple models so as to retain diverse OH models in order to avoid biased update. The competitive efficiency and effectiveness of the proposed online hash models are verified through extensive experiments on several large-scale data sets as compared with related hashing methods.
Online Hashing
Long-Kai Huang,Qiang Yang,Weishi Zheng
Published 2013 in IEEE Transactions on Neural Networks and Learning Systems
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- Publication year
2013
- Venue
IEEE Transactions on Neural Networks and Learning Systems
- Publication date
2013-08-03
- Fields of study
Medicine, Computer Science
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- External record
- Source metadata
Semantic Scholar, PubMed
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