Manifold Denoising by Nonlinear Robust Principal Component Analysis

Published: Nov 10, 2019
Abstract
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 in treating the manifold as a whole as opposed to treating each local region independently? We answer these two questions affirmatively by proposing...
Paper Details
Title
Manifold Denoising by Nonlinear Robust Principal Component Analysis
Published Date
Nov 10, 2019
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