000265378 001__ 265378
000265378 005__ 20190522065147.0
000265378 0247_ $$a10.1109/MSP.2018.2887284$$2doi
000265378 02470 $$a1806.00848$$2ArXiv
000265378 037__ $$aARTICLE
000265378 245__ $$aLearning Graphs From Data: A Signal Representation Perspective
000265378 260__ $$c2019-05-01
000265378 269__ $$a2019-05-01
000265378 336__ $$aJournal Articles
000265378 520__ $$aThe construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus desirable to infer or learn a graph topology from the data. In this article, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective. We further emphasize the conceptual similarities and differences between classical and GSP-based graph-inference methods and highlight the potential advantage of the latter in a number of theoretical and practical scenarios. We conclude with several open issues and challenges that are keys to the design of future signal processing and machine-learning algorithms for learning graphs from data.
000265378 700__ $$0242933$$aDong, Xiaowen$$g193962
000265378 700__ $$0244101$$aThanou, Dorina$$g185309
000265378 700__ $$aRabbat, Michael
000265378 700__ $$0241061$$aFrossard, Pascal$$g101475
000265378 773__ $$tIEEE Signal Processing Magazine$$j36$$k3$$q44-63
000265378 8560_ $$falessandra.bianchi@epfl.ch
000265378 909C0 $$zMarselli, Béatrice$$xU10851$$pLTS4$$mpascal.frossard@epfl.ch$$0252393
000265378 909CO $$qGLOBAL_SET$$pSTI$$particle$$ooai:infoscience.epfl.ch:265378
000265378 960__ $$apascal.frossard@epfl.ch
000265378 961__ $$aalessandra.bianchi@epfl.ch
000265378 973__ $$aEPFL$$sPUBLISHED$$rREVIEWED
000265378 980__ $$aARTICLE
000265378 981__ $$aoverwrite