NDLI: Robust Matrix Factorization with Unknown Noise

Content Provider	IEEE Xplore Digital Library
Author	Deyu Meng De la Torre, F.
Copyright Year	2013
Abstract	Many problems in computer vision can be posed as recovering a low-dimensional subspace from high-dimensional visual data. Factorization approaches to low-rank subspace estimation minimize a loss function between the observed measurement matrix and a bilinear factorization. Most popular loss functions include the $L_{1}$ and $L_{2}$ losses. While $L_{1}$ is optimal for Laplacian distributed noise, $L_{2}$ is optimal for Gaussian noise. However, real data is often corrupted by an unknown noise distribution, which is unlikely to be purely Gaussian or Laplacian. To address this problem, this paper proposes a low-rank matrix factorization problem with a Mixture of Gaussians (MoG) noise. The MoG model is a universal approximator for any continuous distribution, and hence is able to model a wider range of real noise distributions. The parameters of the MoG model can be estimated with a maximum likelihood method, while the subspace is computed with standard approaches. We illustrate the benefits of our approach in extensive synthetic, structure from motion, face modeling and background subtraction experiments.
Sponsorship	IEEE Comput. Soc.
Starting Page	1337
Ending Page	1344
File Size	3803134
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Robustness Principal component analysis Gaussian noise Computational modeling Laplace equations
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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