Elements of information theory
Elements of information theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
A fast fixed-point algorithm for independent component analysis
Neural Computation
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
High-order contrasts for independent component analysis
Neural Computation
Kernel independent component analysis
The Journal of Machine Learning Research
Blind separation of instantaneous mixture of sources based on orderstatistics
IEEE Transactions on Signal Processing
Estimating the entropy of a signal with applications
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
Independent subspace analysis using geodesic spanning trees
ICML '05 Proceedings of the 22nd international conference on Machine learning
Data Driven Image Models through Continuous Joint Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signal Processing - Special issue: Information theoretic signal processing
Fast kernel entropy estimation and optimization
Signal Processing - Special issue: Information theoretic signal processing
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Fast kernel-based independent component analysis
IEEE Transactions on Signal Processing
PCA Gaussianization for image processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Complex independent component analysis by entropy bound minimization
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Independent component analysis by entropy bound minimization
IEEE Transactions on Signal Processing
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
MLPACK: a scalable C++ machine learning library
The Journal of Machine Learning Research
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This paper presents a new algorithm for the independent components analysis (ICA) problem based on an efficient entropy estimator. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated Kullback-Leibler divergence between the joint distribution and the product of the marginal distributions. We pair this approach with efficient entropy estimators from the statistics literature. In particular, the entropy estimator we use is consistent and exhibits rapid convergence. The algorithm based on this estimator is simple, computationally efficient, intuitively appealing, and outperforms other well known algorithms. In addition, the estimator's relative insensitivity to outliers translates into superior performance by our ICA algorithm on outlier tests. We present favorable comparisons to the Kernel ICA, FAST-ICA, JADE, and extended Infomax algorithms in extensive simulations. We also provide public domain source code for our algorithms.