Symmetric Submodular Clustering with Actionable Constraint
Abstract
Clustering with submodular functions has been of interest over the last few years. Symmetric submodular functions are of particular interest as minimizing them is significantly more efficient and they include many commonly used functions in practice viz. graph cuts, mutual information. In this paper, we propose a novel constraint to make clustering actionable which is motivated by applications across multiple domains, and pose the problem of performing symmetric submodular clustering subject to this constraint. We see that obtaining a partition with approximation guarantees is a non-trivial task requiring further work.
Cite
@article{arxiv.1409.6967,
title = {Symmetric Submodular Clustering with Actionable Constraint},
author = {Amit Dhurandhar and Karthik Gurumoorthy},
journal= {arXiv preprint arXiv:1409.6967},
year = {2014}
}
Comments
This research work benefited from the support of the AIRBUS Group Corporate Foundation Chair in Mathematics of Complex Systems established in ICTS-TIFR. appears in Discrete Optimization in Machine Learning, A Neural Information Processing Systems (NIPS) Workshop, 2014