000170039 001__ 170039
000170039 005__ 20190316235227.0
000170039 020__ $$a978-1-4673-0046-9
000170039 02470 $$2ISI$$a000312381403112
000170039 037__ $$aCONF
000170039 245__ $$aLearning of Structured Graph Dictionaries
000170039 269__ $$a2012
000170039 260__ $$c2012
000170039 300__ $$a4
000170039 336__ $$aConference Papers
000170039 520__ $$aWe 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.
000170039 6531_ $$aDictionary learning
000170039 6531_ $$aSignal processing on graphs
000170039 6531_ $$aSparse approximations
000170039 6531_ $$aLTS4
000170039 6531_ $$aLTS2
000170039 700__ $$aZhang, Xuan
000170039 700__ $$0242933$$g193962$$aDong, Xiaowen
000170039 700__ $$0241061$$aFrossard, Pascal$$g101475
000170039 7112_ $$dMarch 25-30, 2012$$cKyoto, Japan$$aInternational Conference on Acoustics, Speech and Signal Processing (ICASSP)
000170039 773__ $$tProceedings of the 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP)
000170039 8564_ $$uhttps://infoscience.epfl.ch/record/170039/files/icassp2012_dictionary_final.pdf$$zn/a$$s93346$$yn/a
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000170039 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:170039
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000170039 917Z8 $$x101475
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000170039 937__ $$aEPFL-CONF-170039
000170039 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000170039 980__ $$aCONF