Learning of structured graph dictionaries

Conference item

We propose a method for learning dictionaries towards sparse approximation of signals defined on vertices of arbitrary graphs. Dictionaries are expected to describe effectively the main spatial and spectral components of the signals of interest, so that their structure is dependent on the graph information and its spectral representation. We first show how operators can be defined for capturing different spectral components of signals on graphs. We then propose a dictionary learning algorithm built on a sparse approximation step and a dictionary update function, which iteratively leads to adapting the structured dictionary to the class of target signals. Experimental results on synthetic and natural signals on graphs demonstrate the efficiency of the proposed algorithm both in terms of sparse approximation and support recovery performance.

Authors: Zhang, X; Dong, X; Frossard, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• Pages: 3373-3376
• Publication date: 2012-08-30
• Acceptance date: 2011-12-22
• Event title: 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012)
• Event location: Kyoto, Japan
• Event website: https://www.2012.ieeeicassp.org/
• Event start date: 2012-03-25
• Event end date: 2012-03-30
• DOI: 10.1109/ICASSP.2012.6288639
• EISSN: 2379-190X
• ISSN: 1520-6149
• EISBN: 9781467300469
• ISBN: 9781467300445
• Language: English
• Keywords: signal processing on graphs; dictionaries; dictionary learning; noise; approximation algorithms; approximation error; training; testing; sparse approximations