Abstract
Independent Vector Analysis (IVA) is a special form of Independent Component Analysis (ICA) in terms of group signals. Most IVA algorithms are developed via optimizing certain contrast functions. The main difficulty of these contrast function based approaches lies in estimating the unknown distribution of sources. On the other hand, tensorial approaches are efficient and richly available to the standard ICA problem, but unfortunately have not been explored considerably for IVA. In this paper, we propose a matrix joint diagonalization approach to solve the complex IVA problem. A conjugate gradient algorithm on an appropriate manifold setting is developed and investigated by several numerical experiments.
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Shen, H., Kleinsteuber, M. (2012). A Matrix Joint Diagonalization Approach for Complex Independent Vector Analysis. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_9
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DOI: https://doi.org/10.1007/978-3-642-28551-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28550-9
Online ISBN: 978-3-642-28551-6
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