NMF for BSS, prevent zero valued sources

1. What is NMF used for?
2. Is NMF probabilistic?
3. Is NMF a clustering algorithm?
4. How does non negative matrix factorization work?

What is NMF used for?

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of high dimensional data as it automatically extracts sparse and meaningful features from a set of nonnegative data vectors.

Is NMF probabilistic?

It was later shown that some types of NMF are an instance of a more general probabilistic model called "multinomial PCA".

Is NMF a clustering algorithm?

NMF is a dimensional reduction method and effective for document clustering, because a term-document matrix is high-dimensional and sparse. The initial matrix of the NMF algorithm is regarded as a clustering result, therefore we can use NMF as a refinement method.

How does non negative matrix factorization work?

Non-Negative Matrix Factorization uses techniques from multivariate analysis and linear algebra. It decomposes the data as a matrix M into the product of two lower ranking matrices W and H. The sub-matrix W contains the NMF basis; the sub-matrix H contains the associated coefficients (weights).