Kernel Treelets
Machine Learning
2019-07-24 v1 Machine Learning
Methodology
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.
Cite
@article{arxiv.1812.04808,
title = {Kernel Treelets},
author = {Hedi Xia and Hector D. Ceniceros},
journal= {arXiv preprint arXiv:1812.04808},
year = {2019}
}