We propose a new algorithm for Independent Component Extraction that extracts one non-Gaussian component and is capable to exploit the non-Gaussianity of background signals without decomposing them into independent components. The algorithm is suitable for situations when the signal to be extracted is determined through initialization; it shows an extra stable convergence when the target component is dominant. In simulations, the proposed method is compared with Natural Gradient and One-unit FastICA, and it yields improved results in terms of the Signal-to-Interference ratio and the number of successful extractions.
CITATION STYLE
Koldovský, Z., Tichavský, P., & Ono, N. (2018). Orthogonally-constrained extraction of independent non-gaussian component from non-gaussian background without ICA. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10891 LNCS, pp. 161–170). Springer Verlag. https://doi.org/10.1007/978-3-319-93764-9_16
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