Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Weighted Parzen Windows for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast fixed-point algorithm for independent component analysis
Neural Computation
High-order contrasts for independent component analysis
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An Information-Theoretic Approach to Neural Computing
An Information-Theoretic Approach to Neural Computing
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Learning generative models of natural images
Neural Networks
Transformation-Invariant Clustering Using the EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Outdoor Color Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Independent component analysis using Potts models
IEEE Transactions on Neural Networks
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
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This work explores blind source separation of fetal electrocardiograms by annealed expectation maximization (AEM). The AEM method improves the traditional EM method by relaxation under an annealing process, which is recruited to avoid trappings of tremendous spurious local minima within an objective function that inversely measures quantitative performance of a demixing structure. The derived objective function depends on the demixing structure as well as a set of membership vectors that represent missing data toward encoding statistical dependency of retrieved independent sources. Under the annealing process, the derived E and M steps are iteratively performed to search for expectations of membership vectors and minimizers of the objective function. The state number of membership vectors is related to modulate discretization of observations. Its effects on extraction of fetal electrocardiograms and reliability of the AEM method for blind source separation are extensively explored by numerical simulations.