AbstractRegularized Multiplicative Algorithms for Nonnegative Matrix Factorization We consider iterative algorithms for nonnegative matrix factorizations which consist in alternating multiplicative update rules. In a variational framework, we use least-squares and Kullback-Leibler fidelity terms as well as different regularizing penalties. The algorithms are derived via the use of surrogate cost functions and of a majorization-minimization approach. This ensures a monotonic decrease of the cost function and allows us to prove convergence of the iterates to a stationary point. We also report results of some numerical simulations. This is joint work with Loic Lecharlier. |