2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-19.4
Paper Title On the Identifiability of Transform Learning for Non-Negative Matrix Factorization
Authors Sixin Zhang, Emmanuel Soubies, Cédric Févotte, IRIT, France
SessionMLSP-19: Non-Negative Matrix Factorization
LocationGather.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
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.