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Principal component analysis (PCA) is a well-known tool in multivariate statistics.
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[author] Jolliffe, I. T.I. T. (2002). Principal component analysis, second ed. Springer Series in Statistics. Springer-Verlag, New York. 2036084 (2004k:62010) \endbibitem
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[author] Efron, BradleyB., Hastie, TrevorT., Johnstone, IainI. and Tibshirani, RobertR. (2004). Least angle regression. Ann. Statist. 32 407–499. With discussion, and a rejoinder by the authors. 10.1214/009053604000000067 2060166 (2005d:62116) \endbibitem
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[author] Kritchman, ShiraS. and Nadler, BoazB. (2008). Determining the number of components in a factor model from limited noisy data. Chemometrics and Intelligent Laboratory Systems 94 19–32. \endbibitem
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[author] Nadler, BoazB. (2008). Finite sample approximation results for principal component analysis: a matrix perturbation approach. Ann. Statist. 36 2791–2817. 10.1214/08-AOS618 2485013 (2010g:62190) \endbibitem
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[author] Josse, JulieJ. and Husson, FrançoisF. (2012). Selecting the number of components in principal component analysis using cross-validation approximations. Comput. Statist. Data Anal. 56 1869–1879. 10.1016/j.csda.2011.11.012 2892383 \endbibitem
2011
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2013
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2013
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2013
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[author] Hastie, TrevorT., Tibshirani, RobertR. and Friedman, JeromeJ. (2009). The elements of statistical learning, second ed. Springer Series in Statistics. Springer, New York Data mining, inference, and prediction. 10.1007/978-0-387-84858-7 2722294 (2012d:62081) \endbibitem
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[author] Mazumder, RahulR., Hastie, TrevorT. and Tibshirani, RobertR. (2010). Spectral regularization algorithms for learning large incomplete matrices. J. Mach. Learn. Res. 11 2287–2322. 2719857 (2011m:62184) \endbibitem
2010
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[author] Gavish, MatanM. and Donoho, David L.D. L. (2014). The optimal hard threshold for singular values is 4 / 3 4/\sqrt{3} . IEEE Trans. Inform. Theory 60 5040–5053. 10.1109/TIT.2014.2323359 3245370 \endbibitem
2014
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