SPARSE ADAPTIVE POSSIBILISTIC CLUSTERING

被引：0
|
作者：
Xenaki, Spyridoula D. [1 ]
Koutroumbas, Konstantinos D. [1 ]
Rontogiannis, Athanasios A. [1 ]
机构：
[1] Natl Observ Athens, IAASARS, GR-15236 Penteli, Greece
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2014年
关键词：
possibilistic clustering; adaptivity; sparsity; ALGORITHM;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
In this paper a new sparse adaptive possibilistic clustering algorithm is presented. The algorithm exhibits high immunity to outliers and provides improved estimates of the cluster representatives by adjusting dynamically certain critical parameters. In addition, the proposed scheme manages - in principle - to estimate the actual number of clusters and by properly imposing sparsity, it becomes capable to deal well with closely located clusters of different densities. Extensive experimental results verify the previous statements.
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页数：5
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