Fetching the paper…
Reading the bibliography…
Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems.
Relations between two sets of variables
H Hotelling · 1936
Earlier work this paper cites.
The canonical correlations of matrix pairs and their numerical computation
Gene. H Golub and Hongyuan Zha · 1992
Earlier work this paper cites.
Matrix Computations (3rd Ed.)
Gene H. Golub and Charles F. Van Loan · 1996
Earlier work this paper cites.
Numerical Linear Algebra
Lloyd N. Trefethen and David Bau · 1997
Earlier work this paper cites.
A probabilistic interpretation of canonical correlation analysis
Francis R. Bach and Michael I. Jordan · 2005
Earlier work this paper cites.
Rate of convergence of steepest descent algorithm
Marina A.Epelman · 2007
Earlier work this paper cites.
Faster least squares approximation
Petros Drineas, Michael W. Mahoney, S. Muthukrishnan, and Tamás Sarlós · 2007
Earlier work this paper cites.
Canonical correlation analysis; an overview with application to learning methods
David R. Hardoon, Sandor Szedmak, Or Szedmak, and John Shawe-taylor · 2007
Earlier work this paper cites.
Multi-view regression via canonical correlation analysis
Sham M. Kakade and Dean P. Foster · 2007
Cited alongside, same era.
A learning algorithm for adaptive canonical correlation analysis of several data sets
Javier Vía, Ignacio Santamaría, and Jesús Pérez · 2007
Cited alongside, same era.
Multi-view dimensionality reduction via canonical correlation analysis
Dean P. Foster, Sham M. Kakade, and Tong Zhang · 2008
Cited alongside, same era.
A least squares formulation for canonical correlation analysis
Liang Sun, Shuiwang Ji, and Jieping Ye · 2008
Cited alongside, same era.
Identifying suspicious urls: An application of large-scale online learning
Justin Ma, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker · 2009
Cited alongside, same era.
Large-Scale Machine Learning with Stochastic Gradient Descent
Léon Bottou · 2010
Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
N. Halko, P. G. Martinsson, and J. A. Tropp · 2011
Later among the works it cites.
An algorithm for the principal component analysis of large data sets
Nathan Halko, Per-Gunnar Martinsson, Yoel Shkolnisky, and Mark Tygert · 2011
Later among the works it cites.
Two step cca: A new spectral method for estimating vector models of words
Paramveer S. Dhillon, Jordan Rodu, Dean P. Foster, and Lyle H. Ungar · 2012
Later among the works it cites.
Efficient dimensionality reduction for canonical correlation analysis
Haim Avron, Christos Boutsidis, Sivan Toledo, and Anastasios Zouzias · 2013
Later among the works it cites.
New subsampling algorithms for fast least squares regression
Paramveer Dhillon, Yichao Lu, Dean P. Foster, and Lyle Ungar · 2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
SVD and Clustering for Unsupervised POS Tagging
Michael Lamar, Yariv Maron, Mark Johnson, and Elie Bienenstock · 2010
Cited alongside, same era.
Multi-view learning of word embeddings via cca
Paramveer S. Dhillon, Dean Foster, and Lyle Ungar · 2011
Cited alongside, same era.
Rie Johnson and Tong Zhang · 2013
Later among the works it cites.
Fast ridge regression with randomized principal component analysis and gradient descent
Lu Yichao and Dean P. Foster · 2014
Closest in time.