Paper Info
Title
A Bi-clustering Framework for Consensus Problems.
Abstract
We consider grouping as a general characterization for problems such as clustering, community detection in networks, and multiple parametric model estimation. We are interested in merging solutions from different grouping algorithms, distilling all their good qualities into a consensus solution. In this paper, we propose a biclustering framework and perspective for reaching consensus in such grouping problems. In particular, this is the first time that the task of finding/fitting multiple parametric models to a dataset is formally posed as a consensus problem. We highlight the equivalence of these tasks and establish the connection with the computational Gestalt program, which seeks to provide a psychologically inspired detection theory for visual events. We also present a simple but powerful biclustering algorithm, specially tuned to the nature of the problem we address, though general enough to handle many different instances inscribed within our characterization. The presentation is accompanied with diverse and extensive experimental results in clustering, community detection, and multiple parametric model estimation in image processing applications.
Year
DOI
Venue
2014
10.1137/140967325
SIAM JOURNAL ON IMAGING SCIENCES
Keywords
DocType
Volume
consensus clustering,community detection,parametric model estimation,biclustering,matrix factorization
Journal
7
Issue
ISSN
Citations 
4
1936-4954
6
PageRank 
References 
Authors
0.42
24
2
Name
Order
Citations
PageRank
Mariano Tepper16812.80
Guillermo Sapiro2148131051.92