Abstract
The research on the robust principal component analysis has been attracting much attention recently. Generally, the model assumes sparse noise and characterizes the error term by the λ1-norm. However, the sparse noise has clustering effect in practice so using a certain λ p -norm simply is not appropriate for modeling. In this paper, we propose a novel method based on sparse Bayesian learning principles and Markov random fields. The method is proved to be very effective for low-rank matrix recovery and contiguous outliers detection, by enforcing the low-rank constraint in a matrix factorization formulation and incorporating the contiguity prior as a sparsity constraint. The experiments on both synthetic data and some practical computer vision applications show that the novel method proposed in this paper is competitive when compared with other state-of-the-art methods.
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This work was partially supported by the National Natural Science Foundation of China (Grant No. 61379014) and Tianjin Research Program of Application Foundation and Advanced Technology (15JCYBJC21700).
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Huan, G., Li, Y. & Song, Z. A novel robust principal component analysis method for image and video processing. Appl Math 61, 197–214 (2016). https://doi.org/10.1007/s10492-016-0128-8
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DOI: https://doi.org/10.1007/s10492-016-0128-8
Keywords
- robust principal component analysis
- sparse Bayesian learning
- Markov random fields
- matrix factorization
- contiguity prior