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

    187185_187185.pdf (1.084Mb)
    Access Status
    Open access
    Authors
    Yang, Z.
    Xiang, Y.
    Xie, S.
    Ding, S.
    Rong, Yue
    Date
    2012
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Yang, Zhuyuan and Xiang, Yong and Xie, Shengli and 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. 23 (10): pp. 1601-1610.
    Source Title
    IEEE Transactions on Neural Networks and Learning Systems
    DOI
    10.1109/TNNLS.2012.2208476
    ISBN
    1045- 9227
    Remarks

    © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/22063
    Collection
    • Curtin Research Publications
    Abstract

    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.

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