Kernel Spectral Clustering
Statistics Theory
2016-06-22 v1 Statistics Theory
Abstract
We investigate the question of studying spectral clustering in a Hilbert space where the set of points to cluster are drawn i.i.d. according to an unknown probability distribution whose support is a union of compact connected components. We modify the algorithm proposed by Ng, Jordan and Weiss in order to propose a new algorithm that automatically estimates the number of clusters and we characterize the convergence of this new algorithm in terms of convergence of Gram operators. We also give a hint of how this approach may lead to learn transformation-invariant representations in the context of image classification.
Cite
@article{arxiv.1606.06519,
title = {Kernel Spectral Clustering},
author = {Ilaria Giulini},
journal= {arXiv preprint arXiv:1606.06519},
year = {2016}
}