Minimum-volume rank-deficient nonnegative matrix factorizations

Leplat, Valentin, Ang, Andersen M.S. and Gillis, Nicolas (2019) Minimum-volume rank-deficient nonnegative matrix factorizations. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. vol. 2019-May, IEEE. pp. 3402-3406 . (doi:10.1109/ICASSP.2019.8682280).

Record type: Conference or Workshop Item (Paper)

Abstract

In recent years, nonnegative matrix factorization (NMF) with volume regularization has been shown to be a powerful identifiable model; for example for hyperspectral unmixing, document classification, community detection and hidden Markov models. In this paper, we show that minimum-volume NMF (min-vol NMF) can also be used when the basis matrix is rank deficient, which is a reasonable scenario for some real-world NMF problems (e.g., for unmixing multispectral images). We propose an alternating fast projected gradient method for min-vol NMF and illustrate its use on rank-deficient NMF problems; namely a synthetic data set and a multispectral image.

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Published date: 12 May 2019

Venue - Dates: 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, , Brighton, United Kingdom, 2019-05-12 - 2019-05-17

Keywords: identifiability, minimum volume, nonnegative matrix factorization, rank deficiency

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