2017
NIPS
NeurIPS 2017
Improved Graph Laplacian via Geometric Self-Consistency
Abstract
We address the problem of setting the kernel bandwidth, epps, used by Manifold Learning algorithms to construct the graph Laplacian. Exploiting the connection between manifold geometry, represented by the Riemannian metric, and the Laplace-Beltrami operator, we set epps by optimizing the Laplacian's ability to preserve the geometry of the data. Experiments show that this principled approach is effective and robust
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— geometric self-consistency
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Hot Topic Early Bird
— spectral method
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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, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Clustering
Machine Learning > Core Methods > Metric Learning
Mathematics & Optimization > Mathematics > Geometry
Mathematics & Optimization > Mathematics > Graph Theory
Machine Learning > Core Methods > Dimensionality Reduction
Deep Learning > Optimization & Theory > Theory