Initialization for Nonnegative Matrix Factorization: a Comprehensive Review
This is an incremental review that addresses the initialization bottleneck in NMF for researchers and practitioners in machine learning.
The paper reviews popular initialization procedures for non-negative matrix factorization (NMF), a non-convex optimization problem where the initial point significantly affects finding efficient local solutions, and presents numerical results to illustrate their performance.
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.