期刊
SIGNAL PROCESSING
卷 91, 期 8, 页码 1963-1972出版社
ELSEVIER
DOI: 10.1016/j.sigpro.2011.02.019
关键词
Independent Component Analysis; Prior knowledge; Constrained ICA
资金
- Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen)
Independent Component Analysis (ICA) aims at blindly decomposing a linear mixture of independent sources. It has lots of applications in diverse research areas. In some applications, there is prior knowledge on the sources and/or the mixing vectors. This prior knowledge can be incorporated in the computation of the independent sources. In this paper we provide an algorithm for so-called spatially constrained ICA (scICA). The algorithm deals with the situation when one mixing vector is exactly known. Also the generalization to more mixing vectors is discussed. Numerical experiments are reported that allow us to assess the improvement in accuracy that can be achieved with these algorithms compared to fully blind ICA and to a previously proposed constrained algorithm. We illustrate the approach with a biomedical application. (C) 2011 Elsevier B.V. All rights reserved.
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