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	A cost-function approach to rival penalized competitive learning (RPCL)
	Ma, Jinwen ; Wang, Taijun
	2006
关键词	clustering analysis competitive learning (CL) convergence cost function gradient descent BASIS FUNCTION NETWORK VECTOR QUANTIZATION NEURAL-NETWORK ALGORITHMS CONVERGENCE APPROXIMATION
英文摘要	Rival penalized competitive learning (RPCL) has been shown to be a useful tool for clustering on a set of sample data in which the number of clusters is unknown. However, the RPCL algorithm was proposed heuristically and is still in lack of a mathematical theory to describe its convergence behavior. In order to solve the convergence problem, we investigate it via a cost-function approach. By theoretical analysis, we prove that a general form of RPCL, called distance-sensitive RPCL (DSRPCL), is associated with the minimization of a cost function on the weight vectors of a competitive learning network. As a DSRPCL process decreases the cost to a local minimum, a number of weight vectors eventually fall into a hypersphere surrounding the sample data, while the other weight vectors diverge to infinity. Moreover, it is shown by the theoretical analysis and simulation experiments that if the cost reduces into the global minimum, a correct number of weight vectors is automatically selected and located around the centers of the actual clusters, respectively. Finally, we apply the DSRPCL algorithms to unsupervised color image segmentation and classification of the wine data.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000239408100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics; SCI(E); EI; 33; 4; 722-737; 36
语种	英语
出处	SCI ; EI
出版者	ieee系统人和控制论汇刊 b辑
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/314953]
专题	数学科学学院
推荐引用方式 GB/T 7714	Ma, Jinwen,Wang, Taijun. A cost-function approach to rival penalized competitive learning (RPCL). 2006-01-01.

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