Fixed-point neural independent component analysis algorithms on the orthogonal group
Future Generation Computer Systems
Learning independent components on the orthogonal group of matrices by retractions
Neural Processing Letters
On the convergence of ICA algorithms with symmetric orthogonalization
IEEE Transactions on Signal Processing
Fixed-point neural independent component analysis algorithms on the orthogonal group
Future Generation Computer Systems
IEEE Transactions on Neural Networks
On the relationships between power iteration, inverse iteration and FastICA
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Statistical analysis of sample-size effects in ICA
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Local convergence analysis of FastICA
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Local stability analysis of maximum nongaussianity estimation in independent component analysis
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Convergence analysis of a discrete-time single-unit gradient ICA algorithm
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Global convergence of FastICA: theoretical analysis and practical considerations
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
On the convergence of ICA algorithms with weighted orthogonal constraint
Digital Signal Processing
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We re-examine a fixed-point algorithm proposed by Hyvarinen for independent component analysis, wherein local convergence is proved subject to an ideal signal model using a square invertible mixing matrix. Here, we derive step-size bounds which ensure monotonic convergence to a local extremum for any initial condition. Our analysis does not assume an ideal signal model but appeals rather to properties of the contrast function itself, and so applies even with noisy data and/or more sources than sensors. The results help alleviate the guesswork that often surrounds step-size selection when the observed signal does not fit an idealized model.