A fast fixed-point algorithm for independent component analysis
Neural Computation
Sparse on-line Gaussian processes
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
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In this paper we address a method of source separation in the case where sources have certain temporal structures. The key contribution in this paper is to incorporate Gaussian process (GP) model into source separation, representing the latent function which characterizes the temporal structure of a source by a random process with Gaussian prior. Marginalizing out the latent function leads to the Gaussian marginal likelihood of source that is plugged in the mutual information-based loss function for source separation. In addition, we also consider the leave-one-out predictive distribution of source, instead of the marginal likelihood, in the same framework. Gradient-based optimization is applied to estimate the demixing matrix through the mutual information minimization, where the marginal distribution of source is replaced by the marginal likelihood of the source or its leave-one-out predictive distribution. Numerical experiments confirm the useful behavior of our method, compared to existing source separation methods.