@inproceedings{5f1e9c27c613459484b4d7dafcfac97c,
title = "Piecewise constant nonnegative matrix factorization",
abstract = "In this paper we propose a non-negative matrix factorization (NMF) model with piecewise-constant activation coefficients. This structure is enforced using a total variation penalty on the rows of the activation matrix. The resulting optimization problem is solved with a majorization-minimization procedure. The proposed algorithm is well suited to analyze data explained by underlying piecewise-constant sequences of states. Its properties are first illustrated using synthetic data. We then use it to solve a video structuring problem that involves both segmentation and clustering tasks. An improvement over a state-of-the-art temporally smoothed NMF algorithm of both clustering and segmentation quality measures is observed.",
keywords = "Non-negative matrix factorization, temporal smoothing, total variation",
author = "N. Seichepine and S. Essid and C. F{\'e}votte and O. Capp{\'e}",
year = "2014",
month = jan,
day = "1",
doi = "10.1109/ICASSP.2014.6854901",
language = "English",
isbn = "9781479928927",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6721--6725",
booktitle = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014",
note = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 ; Conference date: 04-05-2014 Through 09-05-2014",
}