Using underapproximations for sparse nonnegative matrix factorization
✍ Scribed by Nicolas Gillis; François Glineur
- Publisher
- Elsevier Science
- Year
- 2010
- Tongue
- English
- Weight
- 713 KB
- Volume
- 43
- Category
- Article
- ISSN
- 0031-3203
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📜 SIMILAR VOLUMES
Nonnegative matrix factorization has been offered as a fast and effective method for analyzing nonnegative two-mode proximity data. The goal is to structurally represent a nonnegative proximity matrix as the product of two lower-dimensional nonnegative matrices. Goodness of fit is typically measured
In this paper the problem of the efficient implementation of sparse matrix factorization on vector computers is considered. A fine-grain dynamic levelwise scheduling algorithm (DLSA) is proposed. DLSA takes into account the dependences between update operations, thus avoiding the recurrence problem.