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Distributed Signal Processing via Chebyshev Polynomial Approximation
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DOI：10.1109/TSIPN.2018.2824239.png)
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Abstract
En 中文
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.
Keywords:
Chebyshev polynomial approximation
denoising
distributed lasso
distributed optimization
functions of matrices
learning
regularization
signal processing on graphs
spectral graph theory
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