Abstract
One (of) important application of sparse component analysis (SCA) is in underdetermined blind source separation (BSS). Within a probability framework, this paper focuses on recoverability problem of underdetermined BSS based on a two-stage SCA approach. We consider a general case in which both sources and mixing matrix are randomly drawn. First, we present a recoverability probability estimate under the condition that the nonzero entry number of a source column vector is fixed. Next, we define the sparsity degree of a signal, and establish the relationship between the sparsity degree of sources and recoverability probability. Finally, we explain how to use the relationship to guarantee the performance of BSS. Several simulation results have demonstrated the validity of the probability estimation approach.
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Li, Y.Q., Cichocki, A., Amari, S., Ho, D.W.C., Xie, S.L.: Underdetermined blind source separation based on sparse representation. IEEE Trans. on Signal Processing (accepted)
Li, Y.Q., Amari, S., Cichocki, A., Guan, C.T.: Probability Estimation for Recoverability Analysis of Blind Source Separation Based on Sparse Representation. IEEE Trans. on Information Theory (revised) (submitted)
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© 2006 Springer-Verlag Berlin Heidelberg
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Li, Y., Cichocki, A., Amari, Si., Guan, C. (2006). Analysis of Source Sparsity and Recoverability for SCA Based Blind Source Separation. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_103
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DOI: https://doi.org/10.1007/11679363_103
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
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