Journal Article

Query-aware locality-sensitive hashing scheme for lp norm

Ng, Wilfred, Feng, Jianlin, Fang, Qiong, Wang, Wei, Huang, Qiang

2017

Overview

The problem of c-Approximate Nearest Neighbor (c-ANN) search in high-dimensional space is fundamentally important in many applications, such as image database and data mining. Locality-Sensitive Hashing (LSH) and its variants are the well-known indexing schemes to tackle the c-ANN search problem. Traditionally, LSH functions are constructed in a query-oblivious manner, in the sense that buckets are partitioned before any query arrives. However, objects closer to a query may be partitioned into different buckets, which is undesirable. Due to the use of query-oblivious bucket partition, the state-of-the-art LSH schemes for external memory, namely C2LSH and LSB-Forest, only work with approximation ratio of integer c≥2 . In this paper, we introduce a novel concept of query-aware bucket partition which uses a given query as the “anchor” for bucket partition. Accordingly, a query-aware LSH function under a specific lp norm with p∈(0,2] is a random projection coupled with query-aware bucket partition, which removes random shift required by traditional query-oblivious LSH functions. The query-aware bucket partitioning strategy can be easily implemented so that query performance is guaranteed. For each lp norm (p∈(0,2]) , based on the corresponding p-stable distribution, we propose a novel LSH scheme named query-aware LSH (QALSH) for c-ANN search over external memory. Our theoretical studies show that QALSH enjoys a guarantee on query quality. The use of query-aware LSH function enables QALSH to work with any approximation ratio c>1 . In addition, we propose a heuristic variant named QALSH + to improve the scalability of QALSH. Extensive experiments show that QALSH and QALSH + outperform the state-of-the-art schemes, especially in high-dimensional space. Specifically, by using a ratio c<2 , QALSH can achieve much better query quality.

• Publisher: Springer Nature B.V
• Subject: Approximation / Buckets / Data mining / Euclidean space / Norms / Queries / Searching