Divergence-based supervised information feature compression algorithm

	Divergence-based supervised information feature compression algorithm
	Ding, Shi-Fei ; Shi, Zhong-Zhi
	2006
发表期刊	ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1
ISSN	0302-9743
卷号	3971 页码:1421-1426
摘要	In this paper, a novel supervised information feature compression algorithm based on divergence is set up. Firstly, according to the information theory, the concept and its properties of the divergence, i.e. average separability information (ASI) is studied, and a concept of symmetry average separability information (SASI) is proposed, and proved that the SASI here is a kind of distance measure, i.e. the SASI satisfies three requests of distance axiomatization, which can be used to measure the difference degree of a two-class problem. Secondly, based on the SASI. a compression theorem is given, and can be used to design information feature compression algorithm. Based on these discussions, we design a novel supervised information feature compression algorithm based on the SASI. At last, the experimental results demonstrate that the algorithm here is valid and reliable.
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000238112000211
出版者	SPRINGER-VERLAG BERLIN
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/10787
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ding, Shi-Fei
作者单位	1.Shandong Agr Univ, Coll Informat Sci & Engn, Tai An 271018, Peoples R China 2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Ding, Shi-Fei,Shi, Zhong-Zhi. Divergence-based supervised information feature compression algorithm[J]. ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1,2006,3971:1421-1426.
APA	Ding, Shi-Fei,&Shi, Zhong-Zhi.(2006).Divergence-based supervised information feature compression algorithm.ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1,3971,1421-1426.
MLA	Ding, Shi-Fei,et al."Divergence-based supervised information feature compression algorithm".ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1 3971(2006):1421-1426.