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Consider an instance of Euclidean $k$-means or $k$-medians clustering.
Über die zusammenziehende und Lipschitzsche Transformationen
M Kirszbraun · 1934
Earlier work this paper cites.
On general minimax theorems
Maurice Sion · 1958
Earlier work this paper cites.
Least squares quantization in PCM
Stuart Lloyd · 1982
Earlier work this paper cites.
Extensions of Lipschitz mappings into a Hilbert space
William Johnson and Joram Lindenstrauss · 1984
Earlier work this paper cites.
A study of vector quantization for noisy channels
Nariman Farvardin · 1990
Earlier work this paper cites.
Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
Earlier work this paper cites.
Clustering in large graphs and matrices
Petros Drineas, Alan M Frieze, Ravi Kannan, Santosh Vempala, and V Vinay · 1999
Earlier work this paper cites.
Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
Earlier work this paper cites.
Database-friendly random projections: Johnson–Lindenstrauss with binary coins
Dimitris Achlioptas · 2003
Earlier work this paper cites.
Problems and results in extremal combinatorics-I
Noga Alon · 2003
Earlier work this paper cites.
An elementary proof of a theorem of Johnson and Lindenstrauss
Sanjoy Dasgupta and Anupam Gupta · 2003
Earlier work this paper cites.
Empirical processes and random projections
B Klartag, Shahar Mendelson, et al · 2005
Earlier work this paper cites.
Approximate nearest neighbors and the fast Johnson–Lindenstrauss transform
Nir Ailon and Bernard Chazelle · 2006
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Improved approximation algorithms for large matrices via random projections
Tamas Sarlos · 2006
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Fast dimension reduction using Rademacher series on dual BCH codes
Nir Ailon and Edo Liberty · 2009
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Unsupervised feature selection for the k k -means clustering problem
Christos Boutsidis, Petros Drineas, and Michael W Mahoney · 2009
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Random projections for k k -means clustering
Christos Boutsidis, Anastasios Zouzias, and Petros Drineas · 2010
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A sparse Johnson–Lindenstrauss transform
Anirban Dasgupta, Ravi Kumar, and Tamás Sarlós · 2010
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Probability in Banach Spaces: isoperimetry and processes
Michel Ledoux and Michel Talagrand · 2013
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Sparser Johnson-Lindenstrauss transforms
Daniel M Kane and Jelani Nelson · 2014
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New constructions of RIP matrices with fast multiplication and fewer rows
Jelani Nelson, Eric Price, and Mary Wootters · 2014
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Randomized dimensionality reduction for k k -means clustering
Christos Boutsidis, Anastasios Zouzias, Michael W Mahoney, and Petros Drineas · 2015
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Dimensionality reduction for k k -means clustering and low rank approximation
Michael B Cohen, Sam Elder, Cameron Musco, Christopher Musco, and Madalina Persu · 2015
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Data clustering: 50 years beyond k k -means
Anil K Jain · 2010
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Product quantization for nearest neighbor search
Herve Jegou, Matthijs Douze, and Cordelia Schmid · 2011
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New and improved Johnson–Lindenstrauss embeddings via the restricted isometry property
Felix Krahmer and Rachel Ward · 2011
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An almost optimal unrestricted fast Johnson–Lindenstrauss transform
Nir Ailon and Edo Liberty · 2013
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Deterministic feature selection for k k -means clustering
Christos Boutsidis and Malik Magdon-Ismail · 2013
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Turning big data into tiny data: Constant-size coresets for k k -means, pca and projective clustering
Dan Feldman, Melanie Schmidt, and Christian Sohler · 2013
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Michael B Cohen, Jelani Nelson, and David P Woodruff · 2015
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Optimality of the Johnson–Lindenstrauss lemma
Kasper Green Larsen and Jelani Nelson · 2017
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Intro and foundations of data science I
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Assaf Naor · 2018
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Strong coresets for k k -median and subspace approximation: Goodbye dimension
Christian Sohler and David P Woodruff · 2018
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High-dimensional probability: An introduction with applications in data science
Roman Vershynin · 2018
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Oblivious dimension reduction for k k -means – beyond subspaces and the Johnson-Lindenstrauss lemma
Luca Becchetti, Marc Bury, Vincent Cohen-Addad, Fabrizio Grandoni, and Chris Schwiegelshohn · 2019
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