A biclustering framework for consensus problems

Published

Journal Article

© 2014 Society for Industrial and Applied Mathematics. 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.

Full Text

Duke Authors

Cited Authors

  • Tepper, M; Sapiro, G

Published Date

  • November 25, 2014

Published In

Volume / Issue

  • 7 / 4

Start / End Page

  • 2488 - 2552

Electronic International Standard Serial Number (EISSN)

  • 1936-4954

Digital Object Identifier (DOI)

  • 10.1137/140967325

Citation Source

  • Scopus