Compressive PCA for Low-Rank Matrices on Graphs

5 Feb 2016Nauman ShahidNathanael PerraudinGilles PuyPierre Vandergheynst

We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements. The rows and columns of such matrices belong to the span of the first few eigenvectors of the graphs constructed between their rows and columns... (read more)

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