Fast and memory efficient implementation of the exact PNN

被引:44
作者
Fränti, P [1 ]
Kaukoranta, T
Shen, DF
Chang, KS
机构
[1] Univ Joensuu, Dept Comp Sci, FIN-80101 Joensuu, Finland
[2] Univ Turku, Turku Ctr Comp Sci, Dept Comp Sci, FIN-20520 Turku, Finland
[3] Yunlin Univ Sci & Technol, Dept Elect Engn, Yunlin 640, Taiwan
关键词
clustering; codebook generation; image coding; vector quantization;
D O I
10.1109/83.841516
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Straightforward implementation of the exact pairwise nearest neighbor (PNN) algorithm takes O(N-3) time, where N is the number of training vectors. This is rather slow in practical situations. Fortunately, much faster implementation can be obtained with rather simple modifications to the basic algorithm. In this paper, we propose a fast O(tau N-2) time implementation of the exact PNN, where tau is shown to be significantly smaller than N, We give all necessary data structures and implementation details, and give time complexity of the algorithm both in the best case and in the worst case. The proposed implementation achieves the results of the exact PNN with the same O(N) memory requirement.
引用
收藏
页码:773 / 777
页数:5
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