Topology Inference and Signal Representation Using Dictionary Learning

• Published in: 2019 27th European Signal Processing Conference (EUSIPCO) pp. 1 - 5
• Main Authors: Ramezani-Mayiami, Mahmoud, Skretting, Karl
• Format: Conference Proceeding
• Language: English
• Published: EURASIP 01-09-2019
• Subjects: Dictionaries; dictionary learning; Graph signal processing; Laplace equations; Machine learning; multi-variate signal; Signal processing; Signal processing algorithms; signal recovery; Signal representation; Topology; topology inference
• ISSN: 2076-1465
• DOI: 10.23919/EUSIPCO.2019.8902344

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.