Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
High-order contrasts for independent component analysis
Neural Computation
Kernel independent component analysis
The Journal of Machine Learning Research
Beyond independent components: trees and clusters
The Journal of Machine Learning Research
ICA using spacings estimates of entropy
The Journal of Machine Learning Research
Independent subspace analysis using geodesic spanning trees
ICML '05 Proceedings of the 22nd international conference on Machine learning
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Undercomplete Blind Subspace Deconvolution
The Journal of Machine Learning Research
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Controlled complete ARMA independent process analysis
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Robust second-order source separation identifies experimental responses in biomedical imaging
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
| Hi-index | 0.00 |
We propose a new algorithm for independent component and independent subspace analysis problems. This algorithm uses a contrast based on the Schweizer-Wolff measure of pairwise dependence (Schweizer & Wolff, 1981), a non-parametric measure computed on pairwise ranks of the variables. Our algorithm frequently outperforms state of the art ICA methods in the normal setting, is significantly more robust to outliers in the mixed signals, and performs well even in the presence of noise. Our method can also be used to solve independent subspace analysis (ISA) problems by grouping signals recovered by ICA methods. We provide an extensive empirical evaluation using simulated, sound, and image data.