Minimum-volume Rank-deficient Nonnegative Matrix Factorizations

Valentin Leplat; Andersen Man Shun Ang; Nicolas Gillis

ICASSP 2019accepted0 citations

Minimum-volume Rank-deficient Nonnegative Matrix Factorizations

Valentin Leplat, Andersen Man Shun Ang, Nicolas Gillis

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.

BibTeX

@inproceedings{icassp2019_minimumvolumeran,
  title = {Minimum-volume Rank-deficient Nonnegative Matrix Factorizations},
  author = {Valentin Leplat and Andersen Man Shun Ang and Nicolas Gillis},
  booktitle = {ICASSP 2019},
  year = {2019}
}