Self-Taught Hashing for Fast Similarity Search

TL;DR

This paper introduces Self-Taught Hashing, a method that combines unsupervised and supervised learning to generate binary codes for fast similarity search, effectively handling unseen documents and outperforming existing techniques.

Contribution

The paper proposes a novel two-step approach that first finds optimal binary codes with unsupervised learning and then trains classifiers to predict codes for unseen documents.

Findings

Outperforms state-of-the-art techniques on real-world datasets

Uses binarised Laplacian Eigenmap and linear SVM for high-quality codes

Effectively handles unseen documents in similarity search

Abstract

The ability of fast similarity search at large scale is of great importance to many Information Retrieval (IR) applications. A promising way to accelerate similarity search is semantic hashing which designs compact binary codes for a large number of documents so that semantically similar documents are mapped to similar codes (within a short Hamming distance). Although some recently proposed techniques are able to generate high-quality codes for documents known in advance, obtaining the codes for previously unseen documents remains to be a very challenging problem. In this paper, we emphasise this issue and propose a novel Self-Taught Hashing (STH) approach to semantic hashing: we first find the optimal $l$-bit binary codes for all documents in the given corpus via unsupervised learning, and then train $l$ classifiers via supervised learning to predict the $l$-bit code for any query document unseen before. Our experiments on three real-world text datasets show that the proposed approach using binarised Laplacian Eigenmap (LapEig) and linear Support Vector Machine (SVM) outperforms state-of-the-art techniques significantly.