Fast approximate similarity search based on degree-reduced neighborhood graphs

Authors:
Kazuo Aoyama;Kazumi Saito;Hiroshi Sawada;Naonori Ueda
Affiliations:
NTT Communication Science Laboratories, Kyoto, Japan;University of Shizuoka, Shizuoka, Japan;NTT Communication Science Laboratories, Kyoto, Japan;NTT Communication Science Laboratories, Kyoto, Japan
Venue:
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2011

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Abstract

This paper presents a fast approximate similarity search method for finding the most similar object to a given query object from an object set with a dissimilarity with a success probability exceeding a given value. As a search index, the proposed method utilizes a degree-reduced k-nearest neighbor (k-DR) graph constructed from the object set with the dissimilarity, and explores the k-DR graph along its edges using a greedy search (GS) algorithm starting from multiple initial vertices with parallel processing. In the graph-construction stage, the structural parameter k of the k-DR graph is determined so that the probability with which at least one search trial of those with multiple initial vertices succeeds is more than the given success probability. To estimate the greedy-search success probability, we introduce the concept of a basin in the k-DR graph. The experimental results on a real data set verify the approximation scheme and high search performance of the proposed method and demonstrate that it is superior to E2LSH in terms of the expected search cost.