Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
A fast fixed-point algorithm for independent component analysis
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
EURASIP Journal on Applied Signal Processing
Complex-valued ICA based on a pair of generalized covariance matrices
Computational Statistics & Data Analysis
Testing significance of mixing and demixing coefficients in ICA
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Convolutive Blind Signal Separation Based on Asymmetrical Contrast Functions
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Compact CramÉr–Rao Bound Expression for Independent Component Analysis
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind Source Separation: The Location of Local Minima in the Case of Finitely Many Samples
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
A new performance index for ICA: properties, computation and asymptotic analysis
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
| Hi-index | 35.68 |
This paper provides a rigorous statistical analysis of the deflation-based FastICA estimator, where the independent components (ICs) are extracted sequentially. The focus is on two aspects of the estimator: robustness against outliers as measured by the influence function (IF) and on its asymptotic relative efficiency (ARE) as measured by the ratio of the asymptotic variance of the FastICA w.r.t. the optimal maximum likelihood estimator (MLE). The derived compact closed-form expression of the IF reveals the vulnerability of the FastICA estimator to outliers regardless of the used nonlinearity. A cautionary finding is that even a moderate observation towards certain directions can render the estimator deficient in the sense that its separation performance degrades worse than a plain guess. The IF allows the derivation of a compact closed-form expression for the asymptotic covariance matrix of the FastICA estimator and subsequently its asymptotic relative efficiencies (AREs). The ARE figures calculated for some selected source distributions illustrate the fact that the order which the ICs are found is crucial as the accuracy of the previously extracted components can dominantly affect the accuracy of the successive deflation stages.