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Many algorithms in machine learning and computational geometry require, as input, the intrinsic dimension of the manifold that supports the probability distribution of the data.
Curvature measures
Herbert Federer · 1959
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Adaptive Control Processes - A Guided Tour
Richard E. Bellman · 1961
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Manfredo Perdigão do Carmo · 1992
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2. concentration of measure and the classical theorems
J. Michael Steele · 1997
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Assouad, fano, and le cam
Bin Yu · 1997
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Metric Spaces of Non-Positive Curvature
Martin R. Bridson and André Häfliger · 1999
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Empirical geometry of multivariate data: a deconvolution approach
V. I. Koltchinskii · 2000
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Introduction to Topological Manifolds
John Marshall Lee · 2000
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Intrinsic dimension estimation using packing numbers, 2003
Balázs Kégl · 2003
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Introduction to Smooth Manifolds
John Marshall Lee · 2003
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Maximum likelihood estimation of intrinsic dimension
Elizaveta Levina, Peter J Bickel, Elizaveta Levina, and Peter J. Bickel · 2004
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Multiscale estimation of intrinsic dimensionality of data sets
Anna V. Little, Yoon-Mo Jung, and Mauro Maggioni · 2009
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Optimized intrinsic dimension estimator using nearest neighbor graphs
Kumar Sricharan, Raviv Raich, and Alfred O. Hero III · 2010
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Multiscale geometric methods for estimating intrinsic dimension
Anna V Little, Mauro Maggioni, and Lorenzo Rosasco · 2011
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Manifold Learning Theory and Applications
Yunqian Ma and Yun Fu · 2011
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Novel high intrinsic dimensionality estimators
Alessandro Rozza, Gabriele Lombardi, Claudio Ceruti, Elena Casiraghi, and Paola Campadelli · 2012
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Partha Niyogi, Stephen Smale, and Shmuel Weinberger · 2008
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Introduction to Nonparametric Estimation
Alexandre B. Tsybakov · 2008
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1. high-dimensional data
John A. Lee and Michel. Verleysen
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3. estimation of the intrinsic dimension
John A. Lee and Michel. Verleysen
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Intrinsic dimension estimation: Advances and open problems
Francesco Camastra and Antonino Staiano · 2015
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Estimating the Reach of a Manifold
E. Aamari, J. Kim, F. Chazal, B. Michel, A. Rinaldo, and L. Wasserman · 2017
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