An Iterative Algorithm for Regularized Non-negative Matrix Factorizations
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912095629475840 |
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| author | Pav, Steven E. |
| author_facet | Pav, Steven E. |
| contents | We generalize the non-negative matrix factorization algorithm of Lee and Seung to accept a weighted norm, and to support ridge and Lasso regularization. We recast the Lee and Seung multiplicative update as an additive update which does not get stuck on zero values. We apply the companion R package rnnmf to the problem of finding a reduced rank representation of a database of cocktails. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22698 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Iterative Algorithm for Regularized Non-negative Matrix Factorizations Pav, Steven E. Machine Learning Optimization and Control Applications Computation 90-10 G.1.6; J.4 We generalize the non-negative matrix factorization algorithm of Lee and Seung to accept a weighted norm, and to support ridge and Lasso regularization. We recast the Lee and Seung multiplicative update as an additive update which does not get stuck on zero values. We apply the companion R package rnnmf to the problem of finding a reduced rank representation of a database of cocktails. |
| title | An Iterative Algorithm for Regularized Non-negative Matrix Factorizations |
| topic | Machine Learning Optimization and Control Applications Computation 90-10 G.1.6; J.4 |
| url | https://arxiv.org/abs/2410.22698 |