SmallK is a high performance software package for low rank matrix approximation via the nonnegative matrix factorization (NMF). NMF is a constrained low rank approximation where a matrix is approximated by a product of two nonnegative factors. The role of NMF in data analytics has been as significant as the singular value decomposition (SVD). However, due to nonnegativity constraints, NMF has far superior interpretability of its results for many practical problems such as image processing, chemometrics, bioinformatics, topic modeling for text analytics and many more. Our approach to solving the NMF nonconvex optimization problem has proven convergence properties and is one of the most efficient methods developed to date.
Keywords for this software
References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Du, Rundong; Drake, Barry; Park, Haesun: Hybrid clustering based on content and connection structure using joint nonnegative matrix factorization (2019)
- Du, Rundong; Kuang, Da; Drake, Barry; Park, Haesun: DC-NMF: nonnegative matrix factorization based on divide-and-conquer for fast clustering and topic modeling (2017)
Further publications can be found at: https://smallk.github.io/pages_publications.html