000205027 001__ 205027
000205027 005__ 20190812205824.0
000205027 037__ $$aCONF
000205027 245__ $$aLaplacian Matrix Learning for Smooth Graph Signal Representation
000205027 269__ $$a2015
000205027 260__ $$c2015
000205027 336__ $$aConference Papers
000205027 520__ $$aThe construction of a meaningful graph plays a crucial role in the emerging field of signal processing on graphs. In this paper, we address the problem of learning graph Laplacians, which is similar to learning graph topologies, such that the input data form graph signals with smooth variations on the resulting topology. We adopt a factor analysis model for the graph signals and impose a Gaussian probabilistic prior on the latent variables that control these graph signals. We show that the Gaussian prior leads to an efficient representation that favours the smoothness property of the graph signals, and propose an algorithm for learning graphs that enforce such property. Experiments demonstrate that the proposed framework can efficiently infer meaningful graph topologies from only the signal observations.
000205027 6531_ $$aGraph learning
000205027 6531_ $$agraph signal processing
000205027 6531_ $$arepresentation theory
000205027 6531_ $$afactor analysis
000205027 6531_ $$aGaussian prior
000205027 700__ $$0242933$$g193962$$aDong, Xiaowen
000205027 700__ $$0244101$$g185309$$aThanou, Dorina
000205027 700__ $$0241061$$g101475$$aFrossard, Pascal
000205027 700__ $$0240428$$g120906$$aVandergheynst, Pierre
000205027 7112_ $$dApril, 2015$$cBrisbane, Australia$$aIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
000205027 773__ $$tProceedings of IEEE ICASSP
000205027 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/205027/files/icassp_graph_learning_camera_ready.pdf$$s762987
000205027 909C0 $$xU10851$$pLTS4$$0252393
000205027 909C0 $$0252392$$xU10380$$pLTS2
000205027 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:205027
000205027 917Z8 $$x101475
000205027 917Z8 $$x185309
000205027 937__ $$aEPFL-CONF-205027
000205027 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000205027 980__ $$aCONF