Nonparametric Bayesian correlated group regression with applications to image classification
Sparse Bayesian learning has emerged as a powerful tool to tackle various image
classification tasks. The existing sparse Bayesian models usually use independent …
classification tasks. The existing sparse Bayesian models usually use independent …
Sparse Bayesian learning for robust PCA: Algorithms and analyses
J Liu, BD Rao - IEEE Transactions on Signal Processing, 2019 - ieeexplore.ieee.org
In this paper, we propose a new Bayesian model to solve the Robust PCA problem-
recovering the underlying low-rank matrix and sparse matrix from their noisy compositions …
recovering the underlying low-rank matrix and sparse matrix from their noisy compositions …
Bayesian lower bounds for dense or sparse (outlier) noise in the RMT framework
V Ollier, R Boyer, MN El Korso… - 2016 IEEE Sensor Array …, 2016 - ieeexplore.ieee.org
Robust estimation is an important and timely research subject. In this paper, we investigate
performance lower bounds on the mean-square-error (MSE) of any estimator for the …
performance lower bounds on the mean-square-error (MSE) of any estimator for the …
[書籍][B] Robust PCA and Robust Linear Regression via Sparsity Regularization
J Liu - 2019 - search.proquest.com
Robustness to outliers is of paramount importance in data analytics. However, many data
analysis tools are not robust to outliers due to their criterion of minimizing the sum of …
analysis tools are not robust to outliers due to their criterion of minimizing the sum of …