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Ningyu Sha

1 accepted papers

2019

Manifold denoising by Nonlinear Robust Principal Component Analysis

NeurIPS 2019poster

This paper extends robust principal component analysis (RPCA) to nonlinear manifolds. Suppose that the observed data matrix is the sum of a sparse component and a component drawn from some low dimensional manifold. Is it possible to separate them by using similar ideas as RPCA? Is there any benefit…

Cited by 19SourcePDFScholar