Context Aware Nonnegative Matrix Factorization Clustering
This addresses the issue of inconsistent cluster assignments in NMF clustering for data analysis, but it is incremental as it builds on existing NMF methods.
The paper tackles the problem of refining clustering results from nonnegative matrix factorization (NMF) by imposing consistency constraints on cluster assignments, using a game-theoretic framework where objects interact to choose coherent clusters, and results show improved performance over many NMF formulations on common benchmarks.
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.