Sparse Semantic Hashing for Efficient Large Scale Similarity Search
Qifan Wang, Bin Shen, Zhiwei Zhang, Luo Si · 2014
Similarity search, or finding approximate nearest neighbors, is an important technique in various large scale information retrieval applications such as document retrieval. Many recent research demonstrate that hashing methods can achieve promising results for large scale similarity search due to its computational and memory efficiency. However, most existing hashing methods ignore the hidden semantic structure of documents but only use the keyword features (e.g., tf-idf) in hashing codes learning. This paper proposes a novel sparse semantic hashing (SpSH) approach that explores the hidden semantic representation of documents in learning their corresponding hashing codes. In particular, a unified framework is designed for ensuring the hidden semantic structure among the documents by a sparse coding model, while at the same time preserving the document similarity via graph Laplacian. An iterative coordinate descent procedure is then proposed for solving the optimization problem. Extensive experiments on two large scale datasets demonstrate the superior performance of the proposed research over several state-of-the-art hashing methods.