Compressive PCA for Low-Rank Matrices on Graphs.

Nauman Shahid,Nathanael Perraudin,Gilles Puy,Pierre Vandergheynst

IEEE Transactions on Signal and Information Processing over Networks（2017）

引用 5|浏览32

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摘要

We introduce a novel framework for an approximate 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. We leverage this property to recover the nonlinear low-rank structures efficiently from sampled data measurements, with a ...

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关键词

Principal component analysis,Decoding,Manifolds,Laplace equations,Robustness,Scalability

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