000166978 001__ 166978
000166978 005__ 20190316235146.0
000166978 037__ $$aREP_WORK
000166978 245__ $$aDeliverable 3.2: Report on Discovering Structure within Dictionary Learning
000166978 269__ $$a2011
000166978 260__ $$c2011
000166978 336__ $$aReports
000166978 500__ $$aDeliverable 3.2 of the Sparse Models, Algorithms and Learning for Large-Scale Data (SMALL) project founded by the EU FP7 FET-Open program.
000166978 520__ $$aIn this work package (WP), we investigate the possibility of discovering structure within dictionary learning. This could range from exploring groups of atoms that appear in clusters - a form of molecule learning - to learning graphical dependencies across the dictionary elements. In modeling a signal as a sparse combination of atoms, ties between atoms can be enforced. For example harmonic models Gabor dictionaries can be seen as this type of model. Here we aim to explore the "molecule-learning" problem - learning clusters of tied atoms - by generalizing existing dictionary learning methods. Our aim is to show that with this added feature, models tend to be more reliable towards the signals represented.
000166978 6531_ $$aLTS2
000166978 6531_ $$adictionary learning
000166978 6531_ $$astructured sparsity
000166978 700__ $$0242928$$g196462$$aArberet, Simon
000166978 700__ $$aBarchiesi, Daniele
000166978 700__ $$aO'Hanlon, Ken
000166978 8564_ $$uhttps://infoscience.epfl.ch/record/166978/files/deliverable_3.2.pdf$$zn/a$$s232164$$yn/a
000166978 909C0 $$xU10380$$0252392$$pLTS2
000166978 909CO $$qGLOBAL_SET$$pSTI$$ooai:infoscience.tind.io:166978$$preport
000166978 917Z8 $$x196462
000166978 937__ $$aEPFL-REPORT-166978
000166978 973__ $$sPUBLISHED$$aEPFL
000166978 980__ $$aREPORT