Section: New Results

Fast Gradient Methods for Symmetric Nonnegative Matrix Factorization.

We describe fast gradient methods for solving the symmetric nonnegative matrix factorization problem (SymNMF). We use recent results on non-Euclidean gradient methods and show that the SymNMF problem is smooth relative to a well-chosen Bregman divergence. This approach provides a simple hyperparameter-free method which comes with theoretical convergence guarantees. We also discuss accelerated variants. Numerical experiments on clustering problems show that our algorithm scales well and reaches both state of the art convergence speed and clustering accuracy for SymNMF methods.