Paper Info
Title
Learning of structured graph dictionaries
Abstract
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.
Year
DOI
Venue
2012
10.1109/ICASSP.2012.6288639
ICASSP
Keywords
Field
DocType
natural signals,signal representation,spectral components,synthetic signals,graph information,learning (artificial intelligence),dictionary learning algorithm,signal processing on graphs,sparse approximation step,target signals,dictionaries,dictionary learning,main spatial components,sparse approximations,recovery performance,graph theory,spectral representation,structured graph dictionaries,dictionary update function,approximation error,learning artificial intelligence,approximation algorithms,noise,testing
Graph theory,Approximation algorithm,Graph,Pattern recognition,Vertex (geometry),K-SVD,Computer science,Sparse approximation,Theoretical computer science,Artificial intelligence,Operator (computer programming),Approximation error
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4673-0044-5
978-1-4673-0044-5
3
PageRank 
References 
Authors
0.48
0
3
Name
Order
Citations
PageRank
Xuan Zhang130.48
Xiaowen Dong224922.07
Pascal Frossard33015230.41