Poster: High Performance Low Rank Approximation for Scalable Data Analytics

With the advent of internet-scale data, the data mining and machine learning community has adopted Nonnegative Matrix Factorization (NMF) for performing numerous tasks such as topic modeling, background separation from video data, hyper-spectral imaging, web-scale clustering, and community detection. The goals of this project are to develop efficient parallel algorithms for computing nonnegative matrix and tensor factorizations (NMF and NTF) and their variants using a unified framework, and to produce a software package called Parallel Low-rank Approximation with Nonnegative Constraints (PLANC) that delivers the high performance, flexibility, and scalability necessary to tackle the ever-growing size of today's data sets. The algorithms have been generalized to NTF problems and extend the class of algorithms we can efficiently parallelize; our software framework allows end-users to use and extend our techniques.