Abstract
We describe an ICA method based on second order statistics which was originally developed for the separation of components in astrophysical images but is appropriate in contexts where accuracy and versatility are of primary importance. It combines several basic ideas of ICA in a new flexible framework designed to deal with complex data scenarios. This paper describes our approach and discusses its implementation in terms of a library of components. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Cardoso, J. F., & Martin, M. (2007). A flexible component model for precision ICA. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4666 LNCS, pp. 1–8). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_1
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