| Paper ID | MLSP-19.4 |
| Paper Title |
On the Identifiability of Transform Learning for Non-Negative Matrix Factorization |
| Authors |
Sixin Zhang, Emmanuel Soubies, Cédric Févotte, IRIT, France |
| Session | MLSP-19: Non-Negative Matrix Factorization |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-MFC] Matrix factorizations/completion |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Non-negative matrix factorization with transform learning (TL-NMF) aims at estimating a short-time orthogonal transform that projects temporal data into a domain that is more amenable to NMF than off-the-shelf time-frequency transforms. In this work, we study the identifiability of TL-NMF under the Gaussian composite model.We prove that one can uniquely identify row-spaces of the orthogonal transform by optimizing the likelihood function of themodel. This result is illustrated on a toy source separation problem which demonstrates the ability of TL-NMF to learn a suitable orthogonal basis. |
