Published June 8, 2018
| public
Book Section - Chapter
An Efficient Relaxed Projection Method for Constrained Non-negative Matrix Factorization with Application to the Phase-Mapping Problem in Materials Science
Chicago
Abstract
In recent years, a number of methods for solving the constrained non-negative matrix factorization problem have been proposed. In this paper, we propose an efficient method for tackling the ever increasing size of real-world problems. To this end, we propose a general relaxation and several algorithms for enforcing constraints in a challenging application: the phase-mapping problem in materials science. Using experimental data we show that the proposed method significantly outperforms previous methods in terms of ℓ_2-norm error and speed.
Additional Information
© 2018 Springer International Publishing AG, part of Springer Nature. First Online: 08 June 2018. Work supported by an NSF Expedition award for Computational Sustainability (CCF-1522054), NSF Computing Research Infrastructure (CNS-1059284), NSF Inspire (1344201), a MURI/AFOSR grant (FA9550), and a grant from the Toyota Research Institute.Additional details
- Eprint ID
- 86903
- DOI
- 10.1007/978-3-319-93031-2_4
- Resolver ID
- CaltechAUTHORS:20180607-155251991
- NSF
- CCF-1522054
- NSF
- CNS-1059284
- NSF
- CNS-1059284
- NSF
- IIS-1344201
- Air Force Office of Scientific Research (AFOSR)
- FA9550
- Toyota Research Institute
- Created
-
2018-06-08Created from EPrint's datestamp field
- Updated
-
2021-11-15Created from EPrint's last_modified field
- Caltech groups
- JCAP
- Series Name
- Lecture Notes in Computer Science
- Series Volume or Issue Number
- 10848