What is it about?
Furthest Neighbor Search (FNS) is a fundamental problem with wide applications in many fields, such as data mining and pattern recognition. In this paper, we introduce a novel concept of Reverse Locality-Sensitive Hashing (RLSH) family designed for FNS. Accordingly, we propose a novel LSH scheme RQALSH for high-dimensional FNS over external memory. Experimental results validate the efficiency and effectiveness of RQALSH.
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Why is it important?
Existing hashing schemes for FNS are designed for internal memory. The existing techniques for external memory, such as the furthest point Voronoi diagram and the tree-based methods, are only suitable for low-dimensional cases. In this paper, we propose the first provable LSH scheme for high-dimensional FNS over external memory, which might be insightful for the young researchers to continue work on this topic.
Perspectives
I hope this paper could bring new thought to people who design basic data structures for FNS problem or other fundamental similarity search problems.
Dr Qiang Huang
National University of Singapore
Read the Original
This page is a summary of: Reverse Query-Aware Locality-Sensitive Hashing for High-Dimensional Furthest Neighbor Search, April 2017, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/icde.2017.66.
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