000211554 001__ 211554
000211554 005__ 20190416220325.0
000211554 037__ $$aCONF
000211554 245__ $$aRobust Principal Component Analysis on Graphs
000211554 269__ $$a2015
000211554 260__ $$c2015
000211554 336__ $$aConference Papers
000211554 520__ $$aPrincipal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA solves the first issue with a sparse penalty term. The second issue can be handled with the matrix factorization model, which is however non-convex. Besides, PCA based clustering can also be enhanced by using a graph of data similarity. In this article, we introduce a new model called "Robust PCA on Graphs" which incorporates spectral graph regularization into the Robust PCA framework. Our proposed model benefits from 1) the robustness of principal components to occlusions and missing values, 2) enhanced low-rank recovery, 3) improved clustering property due to the graph smoothness assumption on the low-rank matrix, and 4) convexity of the resulting optimization problem. Extensive experiments on 8 benchmark, 3 video and 2 artificial datasets with corruptions clearly reveal that our model outperforms 10 other state-of-the-art models in its clustering and low-rank recovery tasks.
000211554 700__ $$0248142$$g232886$$aShahid, Nauman
000211554 700__ $$0246432$$g211494$$aKalofolias, Vassilis
000211554 700__ $$0241065$$g140163$$aBresson, Xavier
000211554 700__ $$aBronstein, Michael
000211554 700__ $$0240428$$g120906$$aVandergheynst, Pierre
000211554 7112_ $$dDecember 11-18, 2015$$cSantiago, Chile$$aInternational Conference on Computer Vision (ICCV) 2015
000211554 8564_ $$uhttps://infoscience.epfl.ch/record/211554/files/RobustPCAonGraphs.pdf$$zPreprint$$s4003777$$yPreprint
000211554 8564_ $$uhttps://infoscience.epfl.ch/record/211554/files/suppMaterial.pdf$$zsupplementary material$$s4636805$$ysupplementary material
000211554 909C0 $$xU10380$$0252392$$pLTS2
000211554 909CO $$ooai:infoscience.tind.io:211554$$qGLOBAL_SET$$pconf$$pSTI
000211554 917Z8 $$x232886
000211554 937__ $$aEPFL-CONF-211554
000211554 973__ $$rREVIEWED$$sACCEPTED$$aEPFL
000211554 980__ $$aCONF