Kernel Treelets

Hedi Xia; Hector D. Ceniceros

doi:10.1142/s2424922x19500062

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arXiv · 1812.04808

Kernel Treelets

Hedi Xia·Hector D. Ceniceros

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Abstract

A new method for hierarchical clustering is presented. It combines treelets, a particular multiscale decomposition of data, with a projection on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), effectively substitutes the correlation coefficient matrix used in treelets with a symmetric, positive semi-definite matrix efficiently constructed from a kernel function. Unlike most clustering methods, which require data sets to be numeric, KT can be applied to more general data and yield a multi-resolution sequence of basis on the data directly in feature space. The effectiveness and potential of KT in clustering analysis is illustrated with some examples.

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BibTeX RIS

Hedi Xia, Hector D. Ceniceros. 2018-12-12. Kernel Treelets. https://doi.org/10.1142/s2424922x19500062

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