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. © Springer-Verlag 2013.
CITATION STYLE
Zhang, L., Zhang, Y., Zhang, D., & Tian, Q. (2013). Distribution-aware locality sensitive hashing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7733 LNCS, pp. 395–406). https://doi.org/10.1007/978-3-642-35728-2_38
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