2006
NIPS
NeurIPS 2006
Convergence of Laplacian Eigenmaps
Abstract
Geometrically based methods for various tasks of machine learning have attracted considerable attention over the last few years. In this paper we show convergence of eigenvectors of the point cloud Laplacian to the eigen- functions of the Laplace-Beltrami operator on the underlying manifold, thus establishing the ο¬rst convergence results for a spectral dimensionality re- duction algorithm in the manifold setting.
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β NIPS 2006
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β Graph Theory
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Interdisciplinary Bridge
β Machine Learning and Mathematics & Optimization
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Trend Setter
β Embedding Learning
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Keyword Pioneer
β spectral dimensionality reduction
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β Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Robotics, Speech & Audio
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Hot Topic Early Bird
β manifold learning
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Core Methods > Embedding Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Theory
Mathematics & Optimization > Mathematics > Geometry
Mathematics & Optimization > Mathematics > Graph Theory
Machine Learning > Core Methods > Dimensionality Reduction