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The higher order singular value decomposition (HOSVD) of tensors is a generalization of matrix SVD.
The rotation of eigenvectors by a perturbation. iii
Chandler Davis and William Morton Kahan · 1970
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Perturbation bounds in connection with singular value decomposition
Perke Wedin · 1972
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Regularized discriminant analysis
Jerome H Friedman · 1989
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A new method for the model-independent assessment of thickness in three-dimensional images
T Hildebrand and P Rüegsegger · 1997
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Efficient clustering of high-dimensional data sets with application to reference matching
Andrew McCallum, Kamal Nigam, and Lyle H Ungar · 2000
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Multilinear analysis of image ensembles: Tensorfaces
M Vasilescu and Demetri Terzopoulos · 2002
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Processing and visualization for diffusion tensor mri
C-F Westin, Stephan E Maier, Hatsuho Mamata, Arya Nabavi, Ferenc A Jolesz, and Ron Kikinis · 2002
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Discovering local structure in gene expression data: the order-preserving submatrix problem
Amir Ben-Dor, Benny Chor, Richard Karp, and Zohar Yakhini · 2003
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Detecting community structure in networks
Mark EJ Newman · 2004
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Subspace clustering for high dimensional data: a review
Lance Parsons, Ehtesham Haque, and Huan Liu · 2004
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A tensor higher-order singular value decomposition for integrative analysis of DNA microarray data from different studies
Larsson Omberg, Gene H Golub, and Orly Alter · 2007
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High dimensional classification using features annealed independence rules
Jianqing Fan and Yingying Fan · 2008
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Unsupervised multiway data analysis: A literature survey
Evrim Acar and Bülent Yener · 2009
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Random tensors and planted cliques
S Charles Brubaker and Santosh S Vempala · 2009
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On the tensor svd and the optimal low rank orthogonal approximation of tensors
Jie Chen and Yousef Saad · 2009
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Unsupervised learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Tensor decompositions and applications
Tamara G Kolda and Brett W Bader · 2009
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Entrywise bounds for eigenvectors of random graphs
Pradipta Mitra · 2009
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The higher-order singular value decomposition: Theory and an application [lecture notes]
Göran Bergqvist and Erik G Larsson · 2010
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Tensor completion for on-board compression of hyperspectral images
Nan Li and Baoxin Li · 2010
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Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
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Minimax localization of structural information in large noisy matrices
Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, and Aarti Singh · 2011
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Tensor decomposition reveals concurrent evolutionary convergences and divergences and correlations with structural motifs in ribosomal rna
Chaitanya Muralidhara, Andrew M Gross, Robin R Gutell, and Orly Alter · 2011
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Tensor principal component analysis via sum-of-square proofs
Samuel B Hopkins, Jonathan Shi, and David Steurer · 2015
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Fast community detection by score
Jiashun Jin · 2015
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Computational barriers in minimax submatrix detection
Zongming Ma and Yihong Wu · 2015
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Delocalization of eigenvectors of random matrices with independent entries
Mark Rudelson and Roman Vershynin · 2015
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Random weighted projections, random quadratic forms and random eigenvectors
Van Vu and Ke Wang · 2015
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Singular vector perturbation under gaussian noise
Rongrong Wang · 2015
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A higher-order generalized singular value decomposition for comparison of global mrna expression from multiple organisms
Sri Priya Ponnapalli, Michael A Saunders, Charles F Van Loan, and Orly Alter · 2011
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Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets
Qing Xiong, Nicola Ancona, Elizabeth R Hauser, Sayan Mukherjee, and Terrence S Furey · 2012
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A tensor spectral approach to learning mixed membership community models
Animashree Anandkumar, Rong Ge, Daniel Hsu, and Sham Kakade · 2013
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Spectral experts for estimating mixtures of linear regressions
Arun T Chaganty and Percy Liang · 2013
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Most tensor problems are NP-hard
Christopher J Hillar and Lek-Heng Lim · 2013
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Tensor completion for estimating missing values in visual data
Ji Liu, Przemyslaw Musialski, Peter Wonka, and Jieping Ye · 2013
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Tensor decompositions for learning latent variable models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M Kakade, and Matus Telgarsky · 2014
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Interpolating convex and non-convex tensor decompositions via the subspace norm
Qinqing Zheng and Ryota Tomioka · 2015
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T Tony Cai and Anru Zhang · 2016
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Jianqing Fan, Weichen Wang, and Yiqiao Zhong · 2016
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Tensor decomposition for multiple-tissue gene expression experiments
Victoria Hore, Ana Viñuela, Alfonso Buil, Julian Knight, Mark I McCarthy, Kerrin Small, and Jonathan Marchini · 2016
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Asymptotics and concentration bounds for bilinear forms of spectral projectors of sample covariance
Vladimir Koltchinskii and Karim Lounici · 2016
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Perturbation of linear forms of singular vectors under gaussian noise
Vladimir Koltchinskii and Dong Xia · 2016
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Joshua Cape, Minh Tang, and Carey E Priebe · 2017
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Concentration inequalities and moment bounds for sample covariance operators
Vladimir Koltchinskii and Karim Lounici · 2017
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Characterizing spatiotemporal transcriptome of human brain via low rank tensor decomposition
Tianqi Liu, Ming Yuan, and Hongyu Zhao · 2017
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Tensor svd: Statistical and computational limits
Anru Zhang and Dong Xia · 2018
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On polynomial time methods for exact low rank tensor completion
Dong Xia and Ming Yuan · 2019
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