Distribution-Aware Locality Sensitive Hashing

Locality Sensitive Hashing (LSH) has been popularly used in content-based search systems. There exist two main categories of LSH methods: one is to index the original data in an effective way to accelerate search process; the other one is to embed the high-dimensional data into hamming space and perform bit-wise operations to search similar objects. In this paper, we propose a new LSH scheme, called Distribution-Aware LSH (DALSH), to address the problem of lacking adaptation to real data, which is the intrinsic limitation of most LSH methods belong to the former category. In DALSH, a given dataset is embedded into a low-dimensional space with projection vectors learned from data, followed by deriving hash functions from the distribution of the dimension-reduced data. We also present a multi-probe strategy to improve the query performance. Experimental comparisons with the state-of-the-art LSH methods on two high-dimensional datasets demonstrate the efficacy of DALSH.