Fast approximate nearest neighbor search via k-diverse nearest neighbor graph

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Abstract

Approximate nearest neighbor search is a fundamental problem and has been studied for a few decades. Recently graph-based indexing methods have demonstrated their great efficiency, whose main idea is to construct neighborhood graph offline and perform a greedy search starting from some sampled points of the graph online. Most existing graph-based methods focus on either the precise k-nearest neighbor (k-NN) graph which has good exploitation ability, or the diverse graph which has good exploration ability. In this paper, we propose the k-diverse nearest neighbor (k-DNN) graph, which balances the precision and diversity of the graph, leading to good exploitation and exploration abilities simultaneously. We introduce an efficient indexing algorithm for the construction of the k-DNN graph inspired by a well-known diverse ranking algorithm in information retrieval (IR). Experimental results show that our method can outperform both state-of-the-art precise graph and diverse graph methods.

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Xiao, Y., Guo, J., Lan, Y., Xu, J., & Cheng, X. (2018). Fast approximate nearest neighbor search via k-diverse nearest neighbor graph. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 8175–8176). AAAI press. https://doi.org/10.1609/aaai.v32i1.12138

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