Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method
The non-negative matrix factorization (NMF) determines a lower rank approximation of a ¢¤£¦¥¨§�©���� �� � matrix where an ������������������ � interger is given and nonnegativity is imposed on all components of the factors applied to numerous data analysis problems. In applications where the components of the data are necessarily nonnegative such as chemical concentrations in experimental results or pixels in digital images, the NMF provides a more relevant interpretation of the results since it gives non-subtractive combinations of non-negative basis vectors. In this paper, we introduce an algorithm for the NMF based on alternating non-negativity constrained least squares (NMF/ANLS) and the active set based fast algorithm for non-negativity constrained least squares with multiple right hand side vectors, and discuss its convergence properties and a rigorous convergence criterion based on the Karush-Kuhn-Tucker (KKT) conditions. In addition, we also describe algorithms for sparse NMFs and regularized NMF. We show how we impose a sparsity constraint on one of the factors by �� �-norm minimization and discuss its convergence properties. Our algorithms are compared to other commonly used NMF algorithms in the literature on several test data sets in terms of their convergence behavior. £�¥�§�©� � and � £�¥���©� �. The NMF has attracted much attention for over a decade and has been successfully