Topology Inference and Signal Representation Using Dictionary Learning. Ramezani-Mayiami, M. & Skretting, K. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 1-5, Sep., 2019.
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Paper doi abstract bibtex
Paper doi abstract bibtex This paper presents a Joint Graph Learning and Signal Representation algorithm, called JGLSR, for simultaneous topology learning and graph signal representation via a learned over-complete dictionary. The proposed algorithm alternates between three main steps: sparse coding, dictionary learning, and graph topology inference. We introduce the “transformed graph” which can be considered as a projected graph in the transform domain spanned by the dictionary atoms. Simulation results via synthetic and real data show that the proposed approach has a higher performance when compared to the well-known algorithms for joint undirected graph topology inference and signal representation, when there is no information about the transform domain. Five performance measures are used to compare JGLSR with two conventional algorithms and show its higher performance.
@InProceedings{8902344,
author = {M. Ramezani-Mayiami and K. Skretting},
booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
title = {Topology Inference and Signal Representation Using Dictionary Learning},
year = {2019},
pages = {1-5},
abstract = {This paper presents a Joint Graph Learning and Signal Representation algorithm, called JGLSR, for simultaneous topology learning and graph signal representation via a learned over-complete dictionary. The proposed algorithm alternates between three main steps: sparse coding, dictionary learning, and graph topology inference. We introduce the “transformed graph” which can be considered as a projected graph in the transform domain spanned by the dictionary atoms. Simulation results via synthetic and real data show that the proposed approach has a higher performance when compared to the well-known algorithms for joint undirected graph topology inference and signal representation, when there is no information about the transform domain. Five performance measures are used to compare JGLSR with two conventional algorithms and show its higher performance.},
keywords = {graph theory;learning (artificial intelligence);signal representation;transform domain;dictionary atoms;joint undirected graph topology inference;dictionary learning;joint graph learning;simultaneous topology learning;graph signal representation;over-complete dictionary;algorithm alternates;transformed graph;projected graph;JGLSR;signal representation algorithm;topology inference;Dictionaries;Topology;Signal processing algorithms;Laplace equations;Signal processing;Signal representation;Machine learning;Graph signal processing;dictionary learning;topology inference;signal recovery;multi-variate signal},
doi = {10.23919/EUSIPCO.2019.8902344},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570532727.pdf},
}Downloads: 0
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