Beyond RPCA: Flattening complex noise in the frequency domain

Publication Type:
Conference Proceeding
31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 2761 - 2767
Issue Date:
Filename Description Size
14291-66961-1-PB.pdfPublished version1.33 MB
Adobe PDF
Full metadata record
Copyright © 2017, Association for the Advancement of Artificial Intelligence ( 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 ℓ1and 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.
Please use this identifier to cite or link to this item: