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    A Projection-Pursuit-Based Method for Blind Separation of Nonnegative Sources

    189578_189578.pdf (1.394Mb)
    Access Status
    Open access
    Authors
    Yang, Z.
    Xiang, Y.
    Rong, Yue
    Xie, S.
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Yang, Zuyuan and Xiang, Yong and Rong, Yue and Xie, Shengli. 2013. A Projection-Pursuit-Based Method for Blind Separation of Nonnegative Sources. IEEE Transactions on Neural Networks and Learning Systems. 24 (1): pp. 47-57.
    Source Title
    IEEE Transactions on Neural Networks and Learning Systems
    DOI
    10.1109/TNNLS.2012.2224124
    ISSN
    2162-237X
    Remarks

    Copyright © 2013 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/20.500.11937/45510
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a projection pursuit (PP) based method for blind separation of nonnegative sources. First, the available observation matrix is mapped to construct a new mixing model, in which the unaccessible source matrix is normalized to be column-sum-to-one. Then, the PP method is proposed to solve this new model, where the mixing matrix is estimated column by column through tracing the projections to the mapped observations in specified directions, which leads to the recoveryof the sources. The proposed method is much faster than Chan’s method which has similar assumptions to ours, due to the usage of the optimal projection. Also, it is more advantageous inseparating cross-correlated sources than the independence- and uncorrelation-based methods as it does not employ any statistical information of the sources. Furthermore, the new method doesnot require the mixing matrix to be nonnegative. Simulation results demonstrate the superior performance of our method.

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