Kernel Treelets

Code

• github.com/hedixia/kernel_treelet
  none★ 0
• github.com/hedixia/KernelTreelets_v5
  none★ 0

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.

Tasks

Reproductions