Nonnegative blind source separation by sparse component analysis based on determinant measure

Yang, Zuyuan, Xiang, Yong, Xie, Shengli, Ding, Shuxue and Rong, Yue 2012, Nonnegative blind source separation by sparse component analysis based on determinant measure, IEEE transactions on neural networks and learning systems, vol. 23, no. 10, pp. 1601-1610.

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Title Nonnegative blind source separation by sparse component analysis based on determinant measure
Author(s) Yang, Zuyuan
Xiang, Yong
Xie, Shengli
Ding, Shuxue
Rong, Yue
Journal name IEEE transactions on neural networks and learning systems
Volume number 23
Issue number 10
Start page 1601
End page 1610
Total pages 10
Publisher IEEE
Place of publication Piscataway, N. J.
Publication date 2012-10
ISSN 2162-237X
2162-2388
Keyword(s) blind source separation (BSS)
determinant-based sparseness measure
nonnegative sources
sparse component analysis
Summary The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049646

Document type: Journal Article
Collection: School of Information Technology
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