A nearest neighbor search algorithm of high-dimensional data based on sequential NPsim matrix

作     者：李文法 Wang Gongming Ma Nan Liu Hongzhe Li Wenfa;Wang Gongming;Ma Nan;Liu Hongzhe

作者机构：Beijing Key Laboratory of Information Service EngineeringBeijing Union University National Laboratory of BiomacromoleculesInstitute of BiophysicsChinese Academy of Sciences 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2016年第22卷第3期

页      面：241-247页

核心收录：

学科分类：08[工学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Natural Science Foundation of China(No.61300078) the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(No.CIT&TCD201504039) Funding Project for Academic Human Resources Development in Beijing Union University(No.BPHR2014A03,Rk100201510) "New Start"Academic Research Projects of Beijing Union University(No.Hzk10201501) 

主　　题：nearest neighbor search high-dimensional data similarity indexing tree NPsim KD-tree SR-tree Munsell 

摘      要：Problems existin similarity measurement and index tree construction which affect the performance of nearest neighbor search of high-dimensional data. The equidistance problem is solved using NPsim function to calculate similarity. And a sequential NPsim matrix is built to improve indexing performance. To sum up the above innovations,a nearest neighbor search algorithm of high-dimensional data based on sequential NPsim matrix is proposed in comparison with the nearest neighbor search algorithms based on KD-tree or SR-tree on Munsell spectral data set. Experimental results show that the proposed algorithm similarity is better than that of other algorithms and searching speed is more than thousands times of others. In addition,the slow construction speed of sequential NPsim matrix can be increased by using parallel computing.