Context Aware Nonnegative Matrix Factorization Clustering

TL;DR

This paper introduces a game theoretic approach to refine nonnegative matrix factorization clustering results by enforcing consistency constraints, leading to improved clustering performance on benchmark datasets.

Contribution

It presents a novel game theoretic framework that enhances NMF clustering by modeling data points as players and using their interactions to improve final cluster assignments.

Findings

Improves clustering accuracy on benchmark datasets

Enhances NMF results with consistency constraints

Models data points as players in a game

Abstract

In this article we propose a method to refine the clustering results obtained with the nonnegative matrix factorization (NMF) technique, imposing consistency constraints on the final labeling of the data. The research community focused its effort on the initialization and on the optimization part of this method, without paying attention to the final cluster assignments. We propose a game theoretic framework in which each object to be clustered is represented as a player, which has to choose its cluster membership. The information obtained with NMF is used to initialize the strategy space of the players and a weighted graph is used to model the interactions among the players. These interactions allow the players to choose a cluster which is coherent with the clusters chosen by similar players, a property which is not guaranteed by NMF, since it produces a soft clustering of the data. The results on common benchmarks show that our model is able to improve the performances of many NMF formulations.