An iterative hard thresholding approach to ℓ0 sparse Hellinger NMF

Performance of Non-negative Matrix Factorisation (NMF) can be diminished when the underlying factors consist of elements that overlap in the matrix to be factorised. The use of `0 sparsity may improve NMF, however such approaches are generally limited to Euclidean distance. We have previously proposed a stepwise `0 method for Hellinger distance, leading to improved sparse NMF. We extend sparse Hellinger NMF by proposing an alternative Iterative Hard Thresholding sparse approximation method. Experimental validation of the proposed approach is given, with a large improvement over NMF methods when learning is performed on a large dataset.