Please use this identifier to cite or link to this item:
https://doi.org/10.1109/SSP.2018.8450822
| Title: | Minimax Lower Bounds for Nonnegative Matrix Factorization | Authors: | Alsan, M Liu, Z Tan, VYF |
Issue Date: | 29-Aug-2018 | Publisher: | IEEE | Citation: | Alsan, M, Liu, Z, Tan, VYF (2018-08-29). Minimax Lower Bounds for Nonnegative Matrix Factorization. 2018 IEEE Statistical Signal Processing Workshop (SSP) : 328-332. ScholarBank@NUS Repository. https://doi.org/10.1109/SSP.2018.8450822 | Abstract: | © 2018 IEEE. The non-negative matrix factorization (NMF) problem consists in modeling data samples as non-negative linear combinations of non-negative dictionary vectors. While many algorithms for NMF have been proposed, fundamental performance limits of these algorithms are currently not available. This paper plugs this gap by providing lower bounds on the minimax risk (the minimum achievable worst case mean squared error) of estimating the non-negative dictionary matrix under a set of locality and statistical assumptions. | Source Title: | 2018 IEEE Statistical Signal Processing Workshop (SSP) | URI: | https://scholarbank.nus.edu.sg/handle/10635/173662 | ISBN: | 9781538615706 | DOI: | 10.1109/SSP.2018.8450822 |
| Appears in Collections: | Elements Staff Publications |
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| File | Description | Size | Format | Access Settings | Version | |
|---|---|---|---|---|---|---|
| MINIMAX LOWER BOUNDS FOR NONNEGATIVE MATRIX FACTORIZATION.pdf | 554.61 kB | Adobe PDF | CLOSED | None | ||
| 1570431479.pdf | 241.63 kB | Adobe PDF | CLOSED | None |
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