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Manifold embedding algorithms map high-dimensional data down to coordinates in a much lower-dimensional space.
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The correspondence principle and intramolecular dynamics
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Atomic decomposition by basis pursuit
Scott Shaobing Chen, David L Donoho, and Michael A Saunders · 1998
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Nonlinear dimensionality reduction by locally linear embedding
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Nonlinear dimensionality reduction by locally linear embedding
Sam Roweis and Lawrence Saul · 2000
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Variable selection via nonconcave penalized likelihood and its oracle properties
Jianqing Fan and Runze Li · 2001
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Atomic decomposition by basis pursuit
Scott Shaobing Chen and David L. Donoho and Michael A. Saunders · 2001
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Variable selection via nonconcave penalized likelihood and its oracle properties
Jianqing Fan and Runze Li · 2001
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Atomic decomposition by basis pursuit
Scott Shaobing Chen and David L. Donoho and Michael A. Saunders · 2001
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Laplacian eigenmaps and spectral techniques for embedding and clustering
M. Belkin and P. Niyogi · 2002
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Automatic alignment of local representations
Yee Whye Teh and Sam T. Roweis · 2002
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Laplacian eigenmaps and spectral techniques for embedding and clustering
M. Belkin and P. Niyogi · 2002
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Automatic alignment of local representations
Yee Whye Teh and Sam T. Roweis · 2002
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Introduction to Smooth Manifolds
John M. Lee · 2003
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Think globally, fit locally: Unsupervised learning of low dimensional manifolds
Lawrence K. Saul and Sam T. Roweis · 2003
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Introduction to Smooth Manifolds
John M. Lee · 2003
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Think globally, fit locally: Unsupervised learning of low dimensional manifolds
Lawrence K. Saul and Sam T. Roweis · 2003
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Convex Optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Least angle regression
Bradley Efron, Trevor Hastie, Iain Johnstone, Robert Tibshirani, and Others · 2004
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Principal manifolds and nonlinear dimensionality reduction via tangent space alignment
Zhenyue Zhang and Hongyuan Zha · 2004
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Mathematical Analysis I
Vladimir A. Zorich · 2004
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Convex Optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Principal manifolds and nonlinear dimensionality reduction via tangent space alignment
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Mathematical Analysis I
Vladimir A. Zorich · 2004
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Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
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From graphs to manifolds - weak and strong pointwise consistency of graph laplacians
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Scalable algorithms for molecular dynamics simulations on commodity clusters
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Model selection and estimation in regression with grouped variables
M Yuan and Y Lin · 2006
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Quantum ergodicity and mixing of eigenfunctions
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Hui Zou · 2006
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Scalable algorithms for molecular dynamics simulations on commodity clusters
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Diffusion maps
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Model selection and estimation in regression with grouped variables
M Yuan and Y Lin · 2006
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Quantum ergodicity and mixing of eigenfunctions
S Zelditch · 2006
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On model selection consistency of lasso
Peng Zhao and Bin Yu · 2006
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Hui Zou · 2006
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Matthias Hein, Jean-Yves Audibert, and Ulrike von Luxburg · 2007
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Relaxed lasso
Nicolai Meinshausen · 2007
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Graph laplacians and their convergence on random neighborhood graphs
Matthias Hein, Jean-Yves Audibert, and Ulrike von Luxburg · 2007
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Relaxed lasso
Nicolai Meinshausen · 2007
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Least angle and
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Estimating vector fields using sparse basis field expansions
Stefan Haufe, Vadim V Nikulin, Andreas Ziehe, Klaus-Robert Müller, and Guido Nolte · 2009
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Approximating gradients for meshes and point clouds via diffusion metric
Chuanjiang Luo, Issam Safa, and Yusu Wang · 2009
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Section 5. geodesics and the exponential map, December 2009
Weimin Sheng · 2009
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Sharp thresholds for high-dimensional and noisy sparsity recovery using
Martin J. Wainwright · 2009
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Estimating vector fields using sparse basis field expansions
Stefan Haufe, Vadim V Nikulin, Andreas Ziehe, Klaus-Robert Müller, and Guido Nolte · 2009
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Approximating gradients for meshes and point clouds via diffusion metric
Chuanjiang Luo, Issam Safa, and Yusu Wang · 2009
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Section 5. geodesics and the exponential map, December 2009
Weimin Sheng · 2009
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Sharp thresholds for high-dimensional and noisy sparsity recovery using
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Potential energy surfaces: the forces of chemistry
Matthew A. Addicoat and Michael A. Collins · 2010
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Stability selection: Stability selection
Nicolai Meinshausen and Peter Bühlmann · 2010
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New approach for investigating reaction dynamics and rates with ab initio calculations
Kelly L Fleming, Pratyush Tiwary, and Jim Pfaendtner · 2016
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megaman: Manifold Learning with Millions of points
J. McQueen, M. Meila, J. VanderPlas, and Z. Zhang · 2016
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Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel Sauceda, Igor Poltavsky, Kristof T. Schütt, and Klaus-Robert Müller · 2017
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M.K. Elyaderani, S.Jain, J.M.Druce, S.Gonella, and J.D.Haupt · 2017
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Improved graph laplacian via geometric Self-Consistency
Dominique Joncas, Marina Meila, and James McQueen · 2017
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An analysis of the convergence of graph laplacians
Daniel Ting, Ling Huang, and Michael I. Jordan · 2010
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Learning gradients: predictive models that infer geometry and statistical dependence
Q Wu, J Guinney, M Maggioni, and S Mukherjee · 2010
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Potential energy surfaces: the forces of chemistry
Matthew A. Addicoat and Michael A. Collins · 2010
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Stability selection: Stability selection
Nicolai Meinshausen and Peter Bühlmann · 2010
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An analysis of the convergence of graph laplacians
Daniel Ting, Ling Huang, and Michael I. Jordan · 2010
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Learning gradients: predictive models that infer geometry and statistical dependence
Q Wu, J Guinney, M Maggioni, and S Mukherjee · 2010
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Manifold learning using kernel density estimation and local principal components analysis
Kitty Mohammed and Hariharan Narayanan · 2017
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Collective variables for the study of long-time kinetics from molecular trajectories: theory and methods
Frank Noé and Cecilia Clementi · 2017
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Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel Sauceda, Igor Poltavsky, Kristof T. Schütt, and Klaus-Robert Müller · 2017
Later among the works it cites.
M.K. Elyaderani, S.Jain, J.M.Druce, S.Gonella, and J.D.Haupt · 2017
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Improved graph laplacian via geometric Self-Consistency
Dominique Joncas, Marina Meila, and James McQueen · 2017
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Manifold learning using kernel density estimation and local principal components analysis
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Collective variables for the study of long-time kinetics from molecular trajectories: theory and methods
Frank Noé and Cecilia Clementi · 2017
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