Coordinate descent accelerations for signal recovery on scale-free graphs based on total variation minimization. Berger, P., Hannak, G., & Matz, G. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 1689-1693, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex We extend our previous work on learning smooth graph signals from a small number of noisy signal samples. Minimizing the signal's total variation amounts to a non-smooth convex optimization problem. We propose to solve this problem using a combination of Nesterov's smoothing technique and accelerated coordinate descent. The resulting algorithm converges substantially faster, specifically for graphs with vastly varying node degrees (e.g., scale-free graphs).
@InProceedings{8081497,
author = {P. Berger and G. Hannak and G. Matz},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Coordinate descent accelerations for signal recovery on scale-free graphs based on total variation minimization},
year = {2017},
pages = {1689-1693},
abstract = {We extend our previous work on learning smooth graph signals from a small number of noisy signal samples. Minimizing the signal's total variation amounts to a non-smooth convex optimization problem. We propose to solve this problem using a combination of Nesterov's smoothing technique and accelerated coordinate descent. The resulting algorithm converges substantially faster, specifically for graphs with vastly varying node degrees (e.g., scale-free graphs).},
keywords = {convex programming;graph theory;minimisation;signal processing;signal recovery;scale-free graphs;total variation minimization;smooth graph signals;noisy signal samples;nonsmooth convex optimization problem;coordinate descent accelerations;Nesterov smoothing technique;Signal processing algorithms;TV;Convergence;Noise measurement;Smoothing methods;Signal processing;Acceleration},
doi = {10.23919/EUSIPCO.2017.8081497},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347628.pdf},
}Downloads: 0
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