000218449 001__ 218449
000218449 005__ 20190416220330.0
000218449 037__ $$aREP_WORK
000218449 245__ $$aLow-Rank Matrices on Graphs: Generalized Recovery & Applications
000218449 269__ $$a2016
000218449 260__ $$c2016
000218449 300__ $$a37
000218449 336__ $$aReports
000218449 520__ $$aMany real world datasets subsume a linear or non-linear low-rank structure in a very low-dimensional space. Unfortunately, one often has very little or no information about the geometry of the space, resulting in a highly under-determined recovery problem. Under certain circumstances, state-of-the-art algorithms provide an exact recovery for linear low-rank structures but at the expense of highly inscalable algorithms which use nuclear norm. However, the case of non-linear structures remains unresolved. We revisit the problem of low-rank recovery from a totally different perspective, involving graphs which encode pairwise similarity between the data samples and features. Surprisingly, our analysis confirms that it is possible to recover many approximate linear and non-linear low-rank structures with recovery guarantees with a set of highly scalable and efficient algorithms. We call such data matrices as Low-Rank matrices on graphs and show that many real world datasets satisfy this assumption approximately due to underlying stationarity. Our detailed theoretical and experimental analysis unveils the power of the simple, yet very novel recovery framework Fast Robust PCA on Graphs.
000218449 6531_ $$alow-rank matrices
000218449 6531_ $$agraphs
000218449 6531_ $$arobust PCA
000218449 700__ $$0248142$$g232886$$aShahid, Nauman
000218449 700__ $$0247306$$g179669$$aPerraudin, Nathanaël
000218449 700__ $$0240428$$g120906$$aVandergheynst, Pierre
000218449 8564_ $$uhttps://infoscience.epfl.ch/record/218449/files/lrmg_main.pdf$$zn/a$$s9469746$$yn/a
000218449 909C0 $$xU10380$$0252392$$pLTS2
000218449 909CO $$ooai:infoscience.tind.io:218449$$qGLOBAL_SET$$pSTI$$preport
000218449 917Z8 $$x232886
000218449 917Z8 $$x232886
000218449 917Z8 $$x232886
000218449 937__ $$aEPFL-REPORT-218449
000218449 973__ $$aEPFL
000218449 980__ $$aREPORT