Nonnegative matrix underapproximation for robust multiple model fitting

Conference Paper

In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint. NMU results are interesting as, compared to traditional NMF, they present additional sparsity and part-based behavior, explaining unique data features. To show these features in practice, we first present an application to the analysis of climate data. We then present an NMU-based algorithm to robustly fit multiple parametric models to a dataset. The proposed approach delivers state-of-the-art results for the estimation of multiple fundamental matrices and homographies, outperforming other alternatives in the literature and exemplifying the use of efficient NMU computations.

Full Text

Duke Authors

Cited Authors

  • Tepper, M; Sapiro, G

Published Date

  • November 6, 2017

Published In

  • Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017

Volume / Issue

  • 2017-January /

Start / End Page

  • 655 - 663

International Standard Book Number 13 (ISBN-13)

  • 9781538604571

Digital Object Identifier (DOI)

  • 10.1109/CVPR.2017.77

Citation Source

  • Scopus