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Dependent Component Analysis Exploiting Nonnegativity and/or Time-Domain Sparsity

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Blind Source Separation

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSSIGNAL))

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Abstract

It is well-known that many real-world signals are nonnegative [1–8], i.e., their sample values are either zero or greater than zero, such as images. Obviously, nonnegativity is different from the statistical information of sources. Depending on the kinds of dependent sources, the nonnegativity of the source signals could be exploited to carry out dependent component analysis (DCA), i.e., separate these unknown dependent sources from their observed mixtures. If the sources also have certain level of sparsity in time domain, then the nonnegativity and time-domain sparsity of the source signals can be jointly employed to achieve DCA. In this chapter, three classes of dependent component analysis methods are introduced and analyzed, which are the nonnegative sparse representation (NSR) based methods, the convex geometry analysis (CGA) based methods, and the nonnegative matrix factorization (NMF) based methods. These methods either exploit the nonnegativity of the sources or both the nonnegativity and time-domain sparsity of the sources.

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Notes

  1. 1.

    See http://biometrics.cse.msu.edu/fvc04db/index.html.

  2. 2.

    Available: http://www.hgimaging.com/PDF/Kruse-JPL2002-AVIRIS-Hyperion.pdf.

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Author information

Authors and Affiliations

  1. School of Information Technology, Deakin University, Melbourne, Victoria, Australia

    Yong Xiang

  2. College of Computer Science, Sichuan University, Chengdu, Sichuan, China

    Dezhong Peng

  3. Faculty of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China

    Zuyuan Yang

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  1. Yong Xiang
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  2. Dezhong Peng
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Correspondence to Yong Xiang .

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Xiang, Y., Peng, D., Yang, Z. (2015). Dependent Component Analysis Exploiting Nonnegativity and/or Time-Domain Sparsity. In: Blind Source Separation. SpringerBriefs in Electrical and Computer Engineering(). Springer, Singapore. https://doi.org/10.1007/978-981-287-227-2_2

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  • DOI: https://doi.org/10.1007/978-981-287-227-2_2

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