000222948 001__ 222948
000222948 005__ 20190525071229.0
000222948 02470 $$2ArXiv$$aarXiv:1611.04835
000222948 037__ $$aCONF
000222948 245__ $$aMultilinear Low-Rank Tensors on Graphs & Applications
000222948 260__ $$c2016
000222948 269__ $$a2016
000222948 336__ $$aConference Papers
000222948 520__ $$aWe propose a new framework for the analysis of low- rank tensors which lies at the intersection of spectral graph theory and signal processing. As a first step, we present a new graph based low-rank decomposition which approximates the classical low-rank SVD for matrices and multi- linear SVD for tensors. Then, building on this novel decomposition we construct a general class of convex optimization problems for approximately solving low-rank tensor inverse problems, such as tensor Robust PCA. The whole frame- work is named as “Multilinear Low-rank tensors on Graphs (MLRTG)”. Our theoretical analysis shows: 1) MLRTG stands on the notion of approximate stationarity of multi- dimensional signals on graphs and 2) the approximation error depends on the eigen gaps of the graphs. We demonstrate applications for a wide variety of 4 artificial and 12 real tensor datasets, such as EEG, FMRI, BCI, surveillance videos and hyperspectral images. Generalization of the tensor concepts to non-euclidean domain, orders of magnitude speed-up, low-memory requirement and significantly enhanced performance at low SNR are the key aspects of our framework.
000222948 700__ $$g232886$$aShahid, Nauman$$0248142
000222948 700__ $$g264158$$aGrassi, Francesco$$0(EPFLAUTH)264158
000222948 700__ $$g120906$$aVandergheynst, Pierre$$0240428
000222948 8564_ $$uhttps://infoscience.epfl.ch/record/222948/files/cvpr_arxiv.pdf$$zPreprint$$s11183654$$yPreprint
000222948 8560_ $$falain.borel@epfl.ch
000222948 909C0 $$xU10380$$0252392$$pLTS2
000222948 909CO $$ooai:infoscience.tind.io:222948$$qGLOBAL_SET$$pconf$$pSTI
000222948 917Z8 $$x232886
000222948 917Z8 $$x232886
000222948 937__ $$aEPFL-CONF-222948
000222948 973__ $$rREVIEWED$$aEPFL
000222948 980__ $$aCONF