A generative model for separating illumination and reflectance from images
The Journal of Machine Learning Research
A generative model for separating illumination and reflectance from images
The Journal of Machine Learning Research
Variational Bayesian learning for speech modeling and enhancement
Signal Processing
Temporally correlated source separation using variational Bayesian learning approach
Digital Signal Processing
Sequential Bayesian kernel modelling with non-Gaussian noise
Neural Networks
Temporally correlated source separation based on variational Kalman smoother
Digital Signal Processing
Variational Bayesian inference for a nonlinear forward model
IEEE Transactions on Signal Processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
Blind source separation with time series variational Bayes expectation maximization algorithm
Digital Signal Processing
| Hi-index | 35.69 |
We describe a variational Bayes (VB) learning algorithm for generalized autoregressive (GAR) models. The noise is modeled as a mixture of Gaussians rather than the usual single Gaussian. This allows different data points to be associated with different noise levels and effectively provides robust estimation of AR coefficients. The VB framework is used to prevent overfitting and provides model-order selection criteria both for AR order and noise model order. We show that for the special case of Gaussian noise and uninformative priors on the noise and weight precisions, the VB framework reduces to the Bayesian evidence framework. The algorithm is applied to synthetic and real data with encouraging results.