Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs
Machine Learning
2014-09-10 v2
Abstract
We consider a problem of grouping multiple graphs into several clusters using singular value thesholding and non-negative factorization. We derive a model selection information criterion to estimate the number of clusters. We demonstrate our approach using "Swimmer data set" as well as simulated data set, and compare its performance with two standard clustering algorithms.
Cite
@article{arxiv.1406.6315,
title = {Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs},
author = {Nam H. Lee and I-Jeng Wang and Youngser Park and Care E. Priebe and Michael Rosen},
journal= {arXiv preprint arXiv:1406.6315},
year = {2014}
}
Comments
This paper has been withdrawn by the author due to a newer version with overlapping contents