文章基本信息

标题：K-L情報量正則化を用いた線形ファジィクラスタリング法
本地全文：下载
作者：Katsuhiro HONDA ; Akihiro KANDA ; Hidetomo ICHI-HASHI 等
期刊名称：知能と情報
印刷版ISSN：1347-7986
电子版ISSN：1881-7203
出版年度：2003
卷号：15
期号：6
页码：682-692
DOI：10.3156/jsoft.15.682
出版社：Japan Society for Fuzzy Theory and Intelligent Informatics
摘要：FCM-type fuzzy clustering approaches are closely related to Gaussian Mixture Models (GMMs) and the objective function of Fuzzy c-Means with regularization by K-L information (KFCM) is optimized by an EM-like algorithm. In this paper, we propose to apply probabilistic PCA mixture models to linear clustering following the discussion on the relationship between Local PCA and linear fuzzy clustering. Although the proposed method is a kind of the constrained model of KFCM, the algorithm includes a similar formulation with the Fuzzy c-Varieties (FCV) algorithm as a special case. Then the algorithm can be regarded as a modified FCV algorithm with regularization by K-L information, which makes it possible to tune the cluster shapes adaptively.
关键词：Fuzzy c-varieties ; Probabilistic principal component analysis ; Regularization by K-L information