SmallK is a high performance software package for low rank matrix approximation via the nonnegative matrix factorization (NMF). NMF is a 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 constaints, 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.
This work was funded in part by the DARPA XDATA program under contract FA8750-12-2-0309. Our DARPA program manager is Mr. Wade Shen and our XDATA Principal Investigator is Prof. Haesun Park of the Georgia Institute of Technology.
SmallK is under copyright by the Georgia Institute of Technology, 2016. All source code is released under the Apache 2.0 license.