2006
NIPS
NeurIPS 2006
Manifold Denoising
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 re- sults about the convergence of graph Laplacians. In the experiments we show that our method is capable of dealing with non-trivial high-dimensional noise. More- over using the denoising algorithm as pre-processing method we can improve the results of a semi-supervised learning algorithm.
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Conference Pioneer
— NIPS 2006
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Keyword Pioneer
— manifold denoising
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Hot Topic Early Bird
— semi-supervised learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Interdisciplinary Bridge
— Computer Vision and Machine Learning
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Trend Setter
— Image Restoration
Authors
Topics
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Learning Types > Unsupervised Learning
Computer Vision > Processing > Image Restoration
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Core Methods > Graphical Models
Machine Learning > Learning Paradigms > Semi-Supervised Learning