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We introduce a novel random projection technique for efficiently reducing the dimension of very high-dimensional tensors.
On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables
Leon Isserlis · 1918
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The expression of a tensor or a polyadic as a sum of products
Frank L Hitchcock · 1927
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Some mathematical notes on three-mode factor analysis
Ledyard R Tucker · 1966
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Extensions of Lipschitz mappings into a Hilbert space
William B Johnson and Joram Lindenstrauss · 1984
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Two algorithms for nearest-neighbor search in high dimensions
Jon M Kleinberg · 1997
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Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani · 1998
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Learning mixtures of Gaussians
Sanjoy Dasgupta · 1999
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Experiments with random projection
Sanjoy Dasgupta · 2000
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Random projection in dimensionality reduction: applications to image and text data
Ella Bingham and Heikki Mannila · 2001
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Finding frequent items in data streams
Moses Charikar, Kevin Chen, and Martin Farach-Colton · 2002
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Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Dimitris Achlioptas · 2003
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An elementary proof of a theorem of Johnson and Lindenstrauss
Sanjoy Dasgupta and Anupam Gupta · 2003
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The random projection method
Santosh S Vempala · 2005
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Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
Nir Ailon and Bernard Chazelle · 2006
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Very sparse random projections
Ping Li, Trevor J Hastie, and Kenneth W Church · 2006
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The fast Johnson–Lindenstrauss transform and approximate nearest neighbors
Nir Ailon and Bernard Chazelle · 2009
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Nonnegative Matrix and Tensor Factorizations
A. Cichocki, R. Zdunek, A.H. Phan, and S.I. Amari · 2009
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Tensor decompositions and applications
Tamara G Kolda and Brett W Bader · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Tensor regression with applications in neuroimaging data analysis
H. Zhou, L. Li, and H. Zhu · 2013
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Fast multivariate spatio-temporal analysis via low rank tensor learning
Mohammad Taha Bahadori, Qi Rose Yu, and Yan Liu · 2014
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Putting MRFs on a tensor train
Alexander Novikov, Anton Rodomanov, Anton Osokin, and Dmitry Vetrov · 2014
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Matlab tt-toolbox 2.2: Fast multidimensional array operations in tt-format
Ivan Oseledets, Vladimir Kazeev, et al · 2014
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Tensorizing neural networks
Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, and Dmitry P Vetrov · 2015
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Tensor decomposition for signal processing and machine learning
Nicholas D Sidiropoulos, Lieven De Lathauwer, Xiao Fu, Kejun Huang, Evangelos E Papalexakis, and Christos Faloutsos · 2017
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Matrix variate distributions
Arjun K Gupta and Daya K Nagar · 2018
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Expressive power of recurrent neural networks
Valentin Khrulkov, Alexander Novikov, and Ivan Oseledets · 2018
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Tensor random projection for low memory dimension reduction
Yiming Sun, Yang Guo, Joel A Tropp, and Madeleine Udell · 2018
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Matlab tensor toolbox version 3.1
Brett W. Bader, Tamara G. Kolda, et al · 2019
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Faster Johnson-Lindenstrauss transforms via Kronecker products
Ruhui Jin, Tamara G Kolda, and Rachel Ward · 2019
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Multi-dimensional tensor sketch
Yang Shi and Animashree Anandkumar · 2019
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Oblivious sketching of high-degree polynomial kernels
Thomas D Ahle, Michael Kapralov, Jakob BT Knudsen, Rasmus Pagh, Ameya Velingker, David P Woodruff, and Amir Zandieh · 2020
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