Adaptive Multiscale Decomposition of Graph Signals

This paper proposes an adaptive multiscale decomposition algorithm for graph signals. We develop two types of graph signal cost functions: α-sparsity functional and graph signal entropies, to capture the energy compaction of the signal components. The adaptive decomposition can then be constructed b...

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Veröffentlicht in:	IEEE signal processing letters 2016-10, Vol.23 (10), p.1389-1393
Hauptverfasser:	Zheng, Xianwei, Tang, Yuan Yan, Pan, Jianjia, Zhou, Jiantao
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive algorithms Bipartite graph Compaction Compressing Constants Construction costs Cost function Decomposition Discrete wavelet transforms Entropy graph Fourier transform graph signal cost function graph wavelet decomposition (GWD) Graphs Minimum cost Signal processing Signal processing algorithms α-sparsity
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creator	Zheng, Xianwei Tang, Yuan Yan Pan, Jianjia Zhou, Jiantao
description	This paper proposes an adaptive multiscale decomposition algorithm for graph signals. We develop two types of graph signal cost functions: α-sparsity functional and graph signal entropies, to capture the energy compaction of the signal components. The adaptive decomposition can then be constructed by applying a minimum cost constraint during the full subband decomposition. The proposed adaptive decomposition is shown to outperform graph wavelet decomposition in compressing nonpiecewise constant graph signals.
doi_str_mv	10.1109/LSP.2016.2598750
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subjects	Adaptive algorithms Bipartite graph Compaction Compressing Constants Construction costs Cost function Decomposition Discrete wavelet transforms Entropy graph Fourier transform graph signal cost function graph wavelet decomposition (GWD) Graphs Minimum cost Signal processing Signal processing algorithms α-sparsity
title	Adaptive Multiscale Decomposition of Graph Signals
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