Beyond RPCA: Flattening Complex Noise in the Frequency Domain


  • Yunhe Wang Peking University
  • Chang Xu University of Technology Sydney
  • Chao Xu Peking University
  • Dacheng Tao University of Technology Sydney



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 compo- nent. 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 charac- terization. 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 demon- strate that FRPCA is effective for handles complex noise.




How to Cite

Wang, Y., Xu, C., Xu, C., & Tao, D. (2017). Beyond RPCA: Flattening Complex Noise in the Frequency Domain. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1).