  Non-negative matrix factorization (NMF) has become a popular method for
representing meaningful data by extracting a non-negative basis feature from an
observed non-negative data matrix. Some of the unique features of this method
in identifying hidden data put this method amongst the powerful methods in the
machine learning area. The NMF is a known non-convex optimization problem and
the initial point has a significant effect on finding an efficient local
solution. In this paper, we investigate the most popular initialization
procedures proposed for NMF so far. We describe each method and present some of
their advantages and disadvantages. Finally, some numerical results to
illustrate the performance of each algorithm are presented.
