Fast Score Function Estimation with Application in ICA
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Kernel independent component analysis
The Journal of Machine Learning Research
Feature Extraction Based on ICA for Binary Classification Problems
IEEE Transactions on Knowledge and Data Engineering
Identification of discriminative features in the EEG
Intelligent Data Analysis
Target Speech Enhancement in Presence of Jammer and Diffuse Background Noise
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Blind source separation based on cumulants with time and frequency non-properties
IEEE Transactions on Audio, Speech, and Language Processing
Semi-blind suppression of internal noise for hands-free robot spoken dialog system
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Fast kernel density independent component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Multi-level independent component analysis
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A novel kurtosis-dependent parameterized independent component analysis algorithm
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
An efficient score function generation algorithm with information maximization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Analysis of the visual Lombard effect and automatic recognition experiments
Computer Speech and Language
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A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large data sets it is possible to achieve “source adaptivity” by directly estimating from the data the “true” score functions of the sources. We describe an efficient scheme for achieving this by extending the fast density estimation method of Silverman (1982). We show with a real and a synthetic experiment that our method can provide more accurate solutions than state-of-the-art methods when optimization is carried out in the vicinity of the global minimum of the contrast function