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.
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.
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.