Distributed Signal Processing via Chebyshev Polynomial Approximation

Unions of graph multiplier operators are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the application of these operators. The proposed method features approximations of the graph multipliers by shifted Chebyshev polynomials, whose recurrence relations make them readily amenable to distributed computation. We demonstrate how the proposed method can be applied to distributed processing tasks such as smoothing, denoising, inverse filtering, and semi-supervised classification, and show that the communication requirements of the method scale gracefully with the size of the network.


Published in:
IEEE Transactions on Signal and Information Processing over Networks, 4, 4, 736-751
Year:
Dec 01 2018
Publisher:
Piscataway, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
ISSN:
2373-776X
Keywords:
Laboratories:




 Record created 2018-12-13, last modified 2019-01-22


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