A Consensus Approach to Improve NMF Document Clustering.

Nonnegative Matrix Factorization (NMF) which was originally designed for dimensionality reduction has received throughout the years a tremendous amount of attention for clustering purposes in several fields such as image processing or text mining. However, despite its mathematical elegance and simplicity, NMF has exposed a main issue which is its strong sensitivity to starting points, resulting in NMF struggling to converge toward an optimal solution. On another hand, we came to explore and discovered that even after providing a meaningful initialization, selecting the solution with the best local minimum was not always leading to the one having the best clustering quality, but somehow a better clustering could be obtained with a solution slightly off in terms of criterion. Therefore in this paper, we undertake to study the clustering characteristics and quality of a set of NMF best solutions and provide a method delivering a better partition using a consensus made of the best NMF solutions.