Finding the best discoverable solution for a non-negative matrix factorization from a random initialization requires multiple random restarts. NNDSVD has previously been proposed as a “head-start” for NMF, but I show that it is not always a head start, and can be a dangerous local minima. I further explore the use of random uniform or random gaussian models for NMF initialization.
