# Online Hashing

## TL;DR

This paper introduces an online hashing model that efficiently learns from streaming data, using a novel loss function and a structured approach, with theoretical guarantees and multi-model extensions to improve diversity and performance.

## Contribution

It proposes a new online hashing framework with a specialized loss function, structured model optimization, and a multi-model extension to enhance online data processing.

## Key findings

- Effective in processing streaming data
- Outperforms existing hashing methods on large-scale datasets
- Provides theoretical bounds on cumulative loss

## Abstract

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.