Chebyshev Polynomial Approximation for Distributed Signal Processing

Unions of graph Fourier multipliers 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 to the high-dimensional signals collected by sensor networks. The proposed method features approximations of the graph Fourier multipliers by shifted Chebyshev polynomials, whose recurrence relations make them readily amenable to distributed computation. We demonstrate how the proposed method can be used in a distributed denoising task, and show that the communication requirements of the method scale gracefully with the size of the network.


Published in:
Proceedings of IEEE International Conference on Distributed Computing in Sensor Systems
Presented at:
IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), Barcelona, Spain, June 27-29, 2011
Year:
2011
ISBN:
978-1-4577-0513-7
Keywords:
Laboratories:




 Record created 2011-04-26, last modified 2018-09-13

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