Mercer kernel-based clustering in feature space
作者： M. Girolami
关键词: Artificial intelligence 、 Pattern recognition 、 Linear separability 、 Data structure 、 Data transformation (statistics) 、 Unsupervised learning 、 Eigenvalues and eigenvectors 、 Cluster analysis 、 Kernel (linear algebra) 、 Feature vector 、 Mathematics
摘要: The article presents a method for both the unsupervised partitioning of sample data and estimation possible number inherent clusters which generate data. This work exploits notion that performing nonlinear transformation into some high dimensional feature space increases probability linear separability patterns within transformed therefore simplifies associated structure. It is shown eigenvectors kernel matrix defines implicit mapping provides means to estimate computationally simple iterative procedure presented subsequent
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sci-hub.se 参考文章(11)
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