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    Subsampling for graph power spectrum estimation

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    Type
    Conference Paper
    Authors
    Chepuri, Sundeep Prabhakar
    Leus, Geert
    KAUST Grant Number
    OSR-2015-Sensors-2700
    Date
    2016-10-06
    Online Publication Date
    2016-10-06
    Print Publication Date
    2016-07
    Permanent link to this record
    http://hdl.handle.net/10754/623600
    
    Metadata
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    Abstract
    In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.
    Citation
    Chepuri SP, Leus G (2016) Subsampling for graph power spectrum estimation. 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). Available: http://dx.doi.org/10.1109/sam.2016.7569707.
    Sponsors
    This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
    Conference/Event name
    2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016
    DOI
    10.1109/sam.2016.7569707
    ae974a485f413a2113503eed53cd6c53
    10.1109/sam.2016.7569707
    Scopus Count
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    Publications Acknowledging KAUST Support

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