2015 23rd European Signal Processing Conference (EUSIPCO) 2015
DOI: 10.1109/eusipco.2015.7362807
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Bayesian learning for robust principal component analysis

Abstract: We develop a Bayesian learning method for robust principal component analysis where the main task is to estimate a low-rank matrix from noisy and outlier contaminated measurements. To promote low-rank, we use a structured Gaussian prior that induces correlations among column vectors as well as row vectors of the matrix under estimation. In our method, the noise and outliers are modeled by a combined noise model. The method is evaluated and compared to other methods using synthetic data as well as data from the… Show more

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Cited by 3 publications
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References 16 publications
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