Self-taught hashing for fast similarity search

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Self-taught hashing for fast similarity search
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 SelfTaught 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 us...
Dell Zhang, Jun Wang, Deng Cai, Jinsong Lu
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2010
Authors Dell Zhang, Jun Wang, Deng Cai, Jinsong Lu
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