Journal
SIGNAL PROCESSING
Volume 91, Issue 10, Pages 2314-2322Publisher
ELSEVIER
DOI: 10.1016/j.sigpro.2011.04.016
Keywords
Canonical correlation analysis (CCA); Multiset CCA (MCCA); Independent vector analysis (IVA); Joint blind source separation (JBSS); Cumulant matrix; Generalized joint diagonalization
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Funding
- NSF [NSF-CCF 0635129, NSF-IIS 0612076]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1017718] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1016619] Funding Source: National Science Foundation
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In this paper, we show that the joint blind source separation (JBSS) problem can be solved by jointly diagonalizing cumulant matrices of any order higher than one, including the correlation matrices and the fourth-order cumulant matrices. We introduce an efficient iterative generalized joint diagonalization algorithm such that a series of orthogonal procrustes problems are solved. We present simulation results to show that the new algorithms can reliably solve the permutation ambiguity in JBSS and that they offer superior performance compared with existing multiset canonical correlation analysis (MCCA) and independent vector analysis (IVA) approaches. Experiment on real-world data for separation of fetal heartbeat in electrocardiogram (ECG) data demonstrates a new application of JBSS, and the success of the new algorithms for a real-world problem. (C) 2011 Elsevier B.V. All rights reserved.
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