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A fast fixed-point algorithm for independent component analysis
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High-order contrasts for independent component analysis
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Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Intelligent Signal Processing
Variational mixture of Bayesian independent component analyzers
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Kernel independent component analysis
The Journal of Machine Learning Research
Variational learning of clusters of undercomplete nonsymmetric independent components
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Blind separation of sources that have spatiotemporal variance dependencies
Signal Processing - Special issue on independent components analysis and beyond
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond independent components: trees and clusters
The Journal of Machine Learning Research
ICA using spacings estimates of entropy
The Journal of Machine Learning Research
Topographic Independent Component Analysis
Neural Computation
2005 Special issue: A new classifier based on information theoretic learning with unlabeled data
Neural Networks - 2005 Special issue: IJCNN 2005
Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation
Neural Computation
ICA mixture model algorithm for unsupervised classification of remote sensing imagery
International Journal of Remote Sensing
Semi-Supervised Learning
Consistent independent component analysis and prewhitening
IEEE Transactions on Signal Processing - Part I
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
HOS-Based Semi-Blind Spatial Equalization for MIMO Rayleigh Fading Channels
IEEE Transactions on Signal Processing
Unsupervised image classification, segmentation, and enhancement using ICA mixture models
IEEE Transactions on Image Processing
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
ICA mixtures applied to ultrasonic nondestructive classification of archaeological ceramics
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing in advanced nondestructive materials inspection
Application of independent component analysis for evaluation of ashlar masonry walls
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Nonlinear prediction based on independent component analysis mixture modelling
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
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This paper presents a new procedure for learning mixtures of independent component analyzers. The procedure includes non-parametric estimation of the source densities, supervised-unsupervised learning of the model parameters, incorporation of any independent component analysis (ICA) algorithm into the learning of the ICA mixtures, and estimation of residual dependencies after training for correction of the posterior probability of every class to the testing observation vector. We demonstrate the performance of the procedure in the classification of ICA mixtures of two, three, and four classes of synthetic data, and in the classification of defective materials, consisting of 3D finite element models and lab specimens, in non-destructive testing using the impact-echo technique. The application of the proposed posterior probability correction demonstrates an improvement in the classification accuracy. Semi-supervised learning shows that unlabeled data can degrade the performance of the classifier when they do not fit the generative model. Comparative results of the proposed method and standard ICA algorithms for blind source separation in one and multiple ICA data mixtures show the suitability of the non-parametric ICA mixture-based method for data modeling.