Volume 7 Number 12 (Dec. 2012)
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JSW 2012 Vol.7(12): 2832-2839 ISSN: 1796-217X
doi: 10.4304//jsw.7.12.2832-2839

Vector-Distance and Neighborhood Development for High Dimensional Data

Ping Ling1, Xiangsheng Rong2, Xiangyang You2, Ming Xu3
1College of Computer Science and Technology, Xuzhou Normal University, Xuzhou, 221116, China
2Training Department, Xuzhou Air Force College of P. L. A, Xuzhou 221000, China
3Department of Logistic Command, Xuzhou Air Force College of P. L. A, Xuzhou 221000, China

Abstract—This paper presents a novel distance concept, Vector-Distance (VD) for high dimensional data. VD extends traditional scalar-distance to a vector-like fashion by collecting multi partial distances from diverse angles. These partial distances are derived from random projections, and they preserve individual features of dimensions as much as possible. Based on VD definition, a method family for neighborhood development is proposed, where methods consist of some norm definitions and certain constrains specified for various purposes. Experiments on real datasets verify the quality of neighborhoods produced by the proposed method family better or competitive with the neighborhood produced by the state of the art.

Index Terms—vector-distance (VD), high dimensional data, partial distances, neighborhood developmentbr />


Cite: Ping Ling, Xiangsheng Rong, Xiangyang You, Ming Xu, "Vector-Distance and Neighborhood Development for High Dimensional Data," Journal of Software vol. 7, no. 12, pp. 2832-2839, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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