| Title | Beyond RPCA: Flattening complex noise in the frequency domain |
| Authors | Wang, Yunhe Xu, Chang Xu, Chao Tao, Dacheng |
| Affiliation | Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, China Centre for Artificial Intelligence, University of Technology, Sydney, Australia Cooperative Medianet Innovation Center, Peking University, China |
| Issue Date | 2017 |
| Publisher | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
| Citation | 31st AAAI Conference on Artificial Intelligence, AAAI 2017. 2017, 2761-2767. |
| Abstract | 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 1-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 1and 2-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. Copyright ? 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
| URI | http://hdl.handle.net/20.500.11897/504978 |
| Indexed | EI |
| Appears in Collections: | 信息科学技术学院 机器感知与智能教育部重点实验室 |
