SIAM Journal on Scientific and Statistical Computing
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Kernel independent component analysis
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
ICA using spacings estimates of entropy
The Journal of Machine Learning Research
An analysis of entropy estimators for blind source separation
Signal Processing
Fundamental limitation of frequency domain blind source separation for convolutive mixture of speech
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
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We present a robust algorithm for independent component analysis that uses the sum of marginal quadratic negentropies as a dependence measure. It can handle arbitrary source density functions by using kernel density estimation, but is robust for a small number of samples by avoiding empirical expectation and directly calculating the integration of quadratic densities. In addition, our algorithm is scalable because the gradient of our contrast function can be calculated in O(LN) using the fast Gauss transform, where L is the number of sources and N is the number of samples. In our experiments, we evaluated the performance of our algorithm for various source distributions and compared it with other, well-known algorithms. The results show that the proposed algorithm consistently outperforms the others. Moreover, it is extremely robust to outliers and is particularly more effective when the number of observed samples is small and the number of mixed sources is large.