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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Liu, J.G. Aeron, S. |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Tufts Univ., Medford, MA, USA (Liu, J.G.; Aeron, S.) |
| Abstract | A robust algorithm for non-negative matrix factorization (NMF) is presented in this paper with the purpose of dealing with large-scale data, where the separability assumption is satisfied. In particular, we modify the Linear Programming (LP) algorithm of [6] by introducing a reduced set of constraints for exact NMF. In contrast to the previous approaches, the proposed algorithm does not require the knowledge of factorization rank (extreme rays [3] or topics [5]). Furthermore, motivated by a similar problem arising in the context of metabolic network analysis [16], we consider an entirely different regime where the number of extreme rays or topics can be much larger than the dimension of the data vectors. The performance of the algorithm for different synthetic data sets is provided. |
| Starting Page | 1127 |
| Ending Page | 1130 |
| File Size | 377459 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781479902484 |
| DOI | 10.1109/GlobalSIP.2013.6737093 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-03 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Context Algorithm design and analysis Vectors Robustness Face MATLAB Optimization |
| Content Type | Text |
| Resource Type | Article |
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