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Bayesian robust tensor completion via CP decomposition
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DOI：10.1016/j.patrec.2022.10.005.png)
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The real-world tensor data are inevitably missing and corrupted with noise. Some models of the lowrank tensor factorization (LRTF) add an L1 norm or L2 norm to deal with the sparse or Gaussian noise. However, the real noise are usually complex. We propose a robust Bayesian tensor completion method, called MoG BTC-CP, which could impute the missing data and remove the complex noise simultaneously. The observed tensor is assumed to be the summation of a low-rank tensor and the noise. CP decomposition is proposed to extract the low-rank structure of the tensor. We assume that the noise follows a Mixture of Gaussian (MoG) distribution. A full Bayesian framework together with a Gibbs sampling algorithm is designed to estimate the model. Extensive experiments including synthetic data and real life applications show that MoG BTC-CP outperforms the recently published leading tensor completion and denoising methods.
Keyword:
Robust Tensor completion
Bayesian inference
Complex noise
Mixture of Gaussian (MoG) model
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3.3
论文数:
8.0K
被引数:
1.6W
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