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• W2214758462 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 MovieLens 100Kdataset. Comparisons show that the method empirically provides a significant performance improvement over existing methods." @default.
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• W2214758462 date "2015-08-01" @default.
• W2214758462 modified "2023-10-04" @default.
• W2214758462 title "Bayesian learning for robust principal component analysis" @default.
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• W2214758462 doi "https://doi.org/10.1109/eusipco.2015.7362807" @default.
• W2214758462 hasPublicationYear "2015" @default.
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