Beyond RPCA: Flattening complex noise in the frequency domain

Wang, Y; Xu, C; Tao, D

Beyond RPCA: Flattening complex noise in the frequency domain

Wang, Y Xu, C

Tao, D

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Publication Type:: Conference Proceeding
Citation:: 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 2761 - 2767
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Xu, C https://orcid.org/0000-0002-4756-0609	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 2761 - 2767	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125822
dc.description.abstract	Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Discovering robust low-rank data representations is important in many real-world problems. Traditional robust principal component analysis (RPCA) assumes that the observed data are corrupted by some sparse noise (e.g., Laplacian noise) and utilizes the ℓ1-norm to separate out the noisy component. Nevertheless, as well as simple Gaussian or Laplacian noise, noise in real-world data is often more complex, and thus the ℓ1 and ℓ2-norms are insufficient for noise characterization. This paper presents a more flexible approach to modeling complex noise by investigating their properties in the frequency domain. Although elements of a noise matrix are chaotic in the spatial domain, the absolute values of its alternative coefficients in the frequency domain are constant w.r.t. their variance. Based on this observation, a new robust PCA algorithm is formulated by simultaneously discovering the low-rank and noisy components. Extensive experiments on synthetic data and video background subtraction demonstrate that FRPCA is effective for handles complex noise.	en_US
dc.relation.ispartof	31st AAAI Conference on Artificial Intelligence, AAAI 2017	en_US
dc.title	Beyond RPCA: Flattening complex noise in the frequency domain	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Discovering robust low-rank data representations is important in many real-world problems. Traditional robust principal component analysis (RPCA) assumes that the observed data are corrupted by some sparse noise (e.g., Laplacian noise) and utilizes the ℓ1-norm to separate out the noisy component. Nevertheless, as well as simple Gaussian or Laplacian noise, noise in real-world data is often more complex, and thus the ℓ1 and ℓ2-norms are insufficient for noise characterization. This paper presents a more flexible approach to modeling complex noise by investigating their properties in the frequency domain. Although elements of a noise matrix are chaotic in the spatial domain, the absolute values of its alternative coefficients in the frequency domain are constant w.r.t. their variance. Based on this observation, a new robust PCA algorithm is formulated by simultaneously discovering the low-rank and noisy components. Extensive experiments on synthetic data and video background subtraction demonstrate that FRPCA is effective for handles complex noise.

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http://hdl.handle.net/10453/125822