Manifold Denoising by Nolinear Robust Principal Analysis (NRPCA)

This is the repository for code related to 'Manifold Denoising by Nolinear Robust Principal Analysis'. The paper is available on the Arxiv at: https://arxiv.org/abs/1911.03831.

A Python implementation can be found at https://github.com/lyuhe95/NRPCA_python.

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Overview

We extend robust principal component analysis to nolinear manifolds, where we assume that the data matrix contains a sparse component and a component drawn from some low dimensional manifold. We aim at separating both components from noisy data by proposing an optimization framework.

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Descriptions

data: contains data for numerical simulation

dependencies: contains other pacakges used in the implementation

result: contains results for the two examples in the paper

src: contains source codes for NRPCA

Example_MNIST.m: Code for MNIST digits 4&9 classification using NRPCA

Example_SwissRoll.m: Code for 20 dimenssional SwissRoll dataset using NRPCA

setup.m: add paths to run examples.