A Bi-clustering Framework for Consensus Problems

TL;DR

This paper introduces a bi-clustering framework for consensus problems in grouping tasks like clustering, community detection, and model estimation, unifying these under a common approach and demonstrating its effectiveness across various applications.

Contribution

It is the first to formalize multiple parametric model fitting as a consensus problem and presents a versatile bi-clustering algorithm tailored for such tasks.

Findings

Effective in clustering and community detection

Successful in multiple parametric model estimation

Aligns with computational Gestalt principles

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 bi-clustering 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, that seeks to provide a psychologically-inspired detection theory for visual events. We also present a simple but powerful bi-clustering 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.