Manifold Denoising
The MIT Press eBooksPublished 7 September 2007
Matthias Hein, Markus Maier
Citations148
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TL;DR
The presented denoising algorithm is based on a graph-based diffusion process of the point sample and is analyzed using recent results about the convergence of graph Laplacians to improve the results of a semi-supervised learning algorithm.
Abstract
We consider the problem of denoising a noisily sampled submanifold M in ℝd, where the submanifold M is a priori unknown and we are only given a noisy point sample. The presented denoising algorithm is based on a graph-based diffusion process of the point sample. We analyze this diffusion process using recent results about the convergence of graph Laplacians. In the experiments we show that our method is capable of dealing with non-trivial high-dimensional noise. Moreover using the denoising algorithm as pre-processing method we can improve the results of a semi-supervised learning algorithm.
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