An efficient exact nearest neighbor search by compounded embedding

Abstract

Nearest neighbor search (NNS) in high dimensional space is a fundamental and essential operation in applications from many domains, such as machine learning, databases, multimedia and computer vision. In this paper, we first propose a novel and effective distance lower bound computation technique for Euclidean distance by using the combination of linear and non-linear embedding methods. As such, each point in a high dimensional space can be embedded into a low dimensional space such that the distance between two embedded points lower bounds their distance in the original space. Following the filter-and-verify paradigm, we develop an efficient exact NNS algorithm by pruning candidates using the new lower bounding technique and hence reducing the cost of expensive distance computation in high dimensional space. Our comprehensive experiments on 10 real-life and diverse datasets, including image, video, audio and text data, demonstrate that our new algorithm can significantly outperform the state-of-the-art exact NNS techniques.