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Principal components analysis (PCA) is the optimal linear auto-encoder of data, and it is often used to construct features.
On lines and planes of closest fit to systems of points in space
K. Pearson · 1901
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The varimax criterion for analytic rotation in factor analysis
HenryF. Kaiser · 1958
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Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
J. Kruskal · 1964
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Two case studies in the application of principal component analysis
J. N. R. Jeffers · 1967
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A nonlinear mapping for data structure analysis
John Sammon · 1969
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Neural networks and principal component analysis: Learning from examples without local minima
Pierre Baldi and Kurt Hornik · 1988
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Auto-association by multilayer perceptrons and singular value decomposition
H. Bourlard and Y. Kamp · 1988
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Principal components analysis of images via back propagation
Garrison Cottrell and Paul Munro · 1988
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Data compression, feature extraction and autoassociation in feedforward neural networks
Erkki Oja · 1991
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Principal components, minor components and linear neural networks
Erkki Oja · 1992
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Loadings and correlations in the interpretation of principal components
J. Cadima and I. Jolliffe · 1995
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Sparse approximate solutions to linear systems
B. Natarajan · 1995
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Matrix Computations
G.H Golub and C.F. Van Loan · 1996
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Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays
Uri Alon, Naama Barkai, Daniel A Notterman, Kurt Gish, Suzanne Ybarra, Daniel Mack, and Arnold J Levine · 1999
Cited alongside, same era.
Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling
Ash A Alizadeh, Michael B Eisen, R Eric Davis, Chi Ma, Izidore S Lossos, Andreas Rosenwald, Jennifer C Boldrick, Hajeer Sabet, Truc Tran, Xin Yu, et al · 2000
Cited alongside, same era.
Pass efficient algorithms for approximating large matrices
P. Drineas and R. Kannan · 2003
Cited alongside, same era.
A modified principal component technique based on the lasso
N. Trendafilov, I. T. Jolliffe, and M. Uddin · 2003
Cited alongside, same era.
Adaptive sampling and fast low-rank matrix approximation
Deflation methods for sparse pca
Lester W Mackey · 2009
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Generalized power method for sparse principal component analysis
Michel Journée, Yurii Nesterov, Peter Richtárik, and Rodolphe Sepulchre · 2010
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Sparse principal component of a rank-deficient matrix
M. Asteris, D. Papailiopoulos, and G. Karystinos · 2011
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Learning From Data
Yaser Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin · 2012
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Relative errors for deterministic low-rank matrix approximations
M. Ghashami and J. Phillips · 2013
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Truncated power method for sparse eigenvalue problems
Xiao-Tong Yuan and Tong Zhang · 2013
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A. Deshpande and S. Vempala · 2006
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Generalized spectral bounds for sparse LDA
B. Moghaddam, Y. Weiss, and S. Avidan · 2006
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Sparse principal component analysis
H. Zou, T. Hastie, and R. Tibshirani · 2006
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A direct formulation for sparse PCA using semidefinite programming
Alexandre d’Aspremont, Laurent El Ghaoui, Michael I. Jordan, and Gert R. G. Lanckriet · 2007
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Optimal solutions for sparse principal component analysis
Alexandre d’Aspremont, Francis Bach, and Laurent El Ghaoui · 2008
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Sparse regression as a sparse eignvalue problem
B. Moghaddam, A. Gruber, Y. Weiss, and S. Avidan · 2008
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Sparse principal component analysis via regularized low rank matrix approximation
Haipeng Shen and Jianhua Z. Huang · 2008
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Non-negative sparse pca with provable guarantees
M. Asteris, D. Papailiopoulos, and A. Dimakis · 2014
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Optimal cur matrix decompositions
Christos Boutsidis and David Woodruff · 2014
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Near-optimal column-based matrix reconstruction
C. Boutsidis, P. Drineas, and M. Magdon-Ismail · 2014
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Dean Foster, Howard Karloff, and Justin Thaler · 2014
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NP-hardness and inapproximability of sparse pca
M. Magdon-Ismail · 2015
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