Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70833
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Title: Efficient sub-window nearest neighbor search on matrix
Authors: Chan, TN 
Yiu, ML 
Hua, KA
Issue Date: 1-Apr-2017
Source: IEEE transactions on knowledge and data engineering, 1 Apr. 2017, v. 29, no. 4, p. 784-797
Abstract: We study a nearest neighbor search problem on a matrix by its element values. Given a data matrix D and a query matrix q, the sub-window nearest neighbor search problem finds a sub-window of D that is the most similar to q. This problem has a wide range of applications, e.g., geospatial data integration, object detection, and motion estimation. In this paper, we propose an efficient progressive search solution that overcomes the drawbacks of existing solutions. First, we present a generic approach to build level-based lower bound functions on top of basic lower bound functions. Second, we develop a novel lower bound function for a group of sub-windows, in order to boost the efficiency of our solution. Furthermore, we extend our solution to support irregular-shaped queries. Experimental results on real data demonstrate the efficiency of our proposed methods.
Keywords: Nearest neighbor
Similarity search
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on knowledge and data engineering 
ISSN: 1041-4347
EISSN: 1558-2191
DOI: 10.1109/TKDE.2016.2633357
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication T. N. Chan, M. L. Yiu and K. A. Hua, "Efficient Sub-Window Nearest Neighbor Search on Matrix," in IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 4, pp. 784-797, 1 April 2017 is available at https://doi.org/10.1109/TKDE.2016.2633357
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