A high-performance parallel algorithm for nonnegative matrix factorization
Abstract
Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors W and H , for the given input matrix A , such that A ≈ WH . NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient distributed algorithms to solve the problem for big data sets.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 27, 2016
- Source ID
- 10.1145/3016078.2851152
Entities
People
- Grey Ballard
- Haesun Park
- Ramakrishnan Kannan
Organizations
- Air Force Office of Scientific Research
- Defense Advanced Research Projects Agency
- Georgia Tech
- National Science Foundation
- Sandia National Laboratories
- United States Department of Energy