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Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data.
Relations between two sets of variates
H. Hotelling · 1936
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The structure of bivariate distributions
H. Lancaster · 1958
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The general theory of canonical correlation and its relation to functional analysis
E. J. Hannan · 1961
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Polynomial expansions of bivariate distributions
G. Eagleson · 1964
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On estimating regression
E. A. Nadaraya · 1964
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Smooth regression analysis
G. S. Watson · 1964
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Multivariate Analysis
K. V. Mardia, J. T. Kent, and J. M. Bibby · 1979
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Estimating optimal transformations for multiple regression and correlation
L. Breiman and J. H. Friedman · 1985
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Variable kernel density estimation
G. R. Terrell and D. W. Scott · 1992
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X-Ray Microbeam Speech Production Database User’s Handbook Version 1.0 , 1994
J. R. Westbury · 1994
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An optimal algorithm for approximate nearest neighbor searching fixed dimensions
S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu · 1998
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Kernel and nonlinear canonical correlation analysis
P. L. Lai and C. Fyfe · 2000
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A kernel method for canonical correlation analysis
S. Akaho · 2001
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Canonical correlation: A tutorial
M. Borga · 2001
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Nonlinear feature extraction using generalized canonical correlation analysis
T. Melzer, M. Reiter, and H. Bischof · 2001
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Using the Nyström method to speed up kernel machines
C. K. I. Williams and M. Seeger · 2001
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Kernel independent component analysis
F. R. Bach and M. I. Jordan · 2002
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MMSE whitening and subspace whitening
Y. C. Eldar and A. V. Oppenheim · 2003
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Mean shift based clustering in high dimensions: A texture classification example
B. Georgescu, I. Shimshoni, and P. Meer · 2003
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Canonical correlation analysis: An overview with application to learning methods
D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor · 2004
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A probabilistic interpretation of canonical correlation analysis
F. R. Bach and M. I. Jordan · 2005
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Multi-view learning of word embeddings via CCA
P. Dhillon, D. Foster, and L. Ungar · 2011
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An algorithm for the principal component analysis of large data sets
N. Halko, P.-G. Martinsson, Y. Shkolnisky, and M. Tygert · 2011
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Co-regularized multi-view spectral clustering
A. Kumar, P. Rai, and H. Daumé III · 2011
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Kernel CCA for multi-view learning of acoustic features using articulatory measurements
R. Arora and K. Livescu · 2012
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Sparse additive functional and kernel CCA
S. Balakrishnan, K. Puniyani, and J. Lafferty · 2012
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Two manifold problems with applications to nonlinear system identification
B. Boots and G. Gordon · 2012
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Information bottleneck for Gaussian variables
G. Chechik, A. Globerson, N. Tishby, and Y. Weiss · 2005
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Spectral clustering with two views
V. de Sa · 2005
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Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
A. Andoni and P. Indyk · 2006
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Spectral clustering and transductive learning with multiple views
D. Zhou and C. J. C. Burges · 2007
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Visualizing data using t t -SNE
L. J. P. van der Maaten and G. E. Hinton · 2008
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Submanifold density estimation
A. Ozakin and A. Gray · 2009
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Unsupervised metric fusion by cross diffusion
B. Wang, J. Jiang, W. Wang, Z.-H. Zhou, and Z. Tu · 2012
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Deep canonical correlation analysis
G. Andrew, R. Arora, J. Bilmes, and K. Livescu · 2013
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Multi-view CCA-based acoustic features for phonetic recognition across speakers and domains
R. Arora and K. Livescu · 2013
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Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables
M. Bolla · 2013
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Improving image-sentence embeddings using large weakly annotated photo collections
Y. Gong, L. Wang, M. Hodosh, J. Hockenmaier, and S. Lazebnik · 2014
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Common manifold learning using alternating-diffusion
R. R. Lederman and R. Talmon · 2014
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Randomized nonlinear component analysis
D. Lopez-Paz, S. Sra, A. Smola, Z. Ghahramani, and B. Schoelkopf · 2014
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An efficient algorithm for information decomposition and extraction
A. Makur, F. Kozynski, S.-L. Huang, and L. Zheng · 2015
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On deep multi-view representation learning
W. Wang, R. Arora, K. Livescu, and J. Bilmes · 2015
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