Online Hashing
This work addresses the need for online learning in hashing for streaming data, which is incremental as it adapts existing off-line methods to an online setting.
The paper tackles the problem of learning hash functions for sequential or online data by proposing an online hash model with a new loss function and passive-aggressive optimization, achieving competitive efficiency and effectiveness on large-scale datasets compared to related methods.
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 work 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 from a single-model to a multi-model online hashing that trains multiple models so as to retain diverse online hashing 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 datasets as compared to related hashing methods.