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We consider a discriminative learning (regression) problem, whereby the regression function is a convex combination of k linear classifiers.
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1973
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[author] Li, Ker-ChauK.-C. (1992). On principal Hessian directions for data visualization and dimension reduction: another application of Stein’s lemma. Journal of the American Statistical Association 87 1025–1039. \endbibitem
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[author] Liu, Jun SJ. S. (1994). Siegel’s formula via Stein’s identities. Statistics & Probability Letters 21 247–251. \endbibitem
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[author] Cook, R DennisR. D. (1998). Principal Hessian directions revisited. Journal of the American Statistical Association 93 84–94. \endbibitem
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[author] McLachlan, GeoffreyG. and Peel, DavidD. (2004). Finite mixture models. Wiley. com. \endbibitem
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2004
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2006
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2010
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2010
[author] Bartholomew, David JD. J., Knott, MartinM. and Moustaki, IriniI. (2011). Latent variable models and factor analysis: A unified approach 899. Wiley. com. \endbibitem
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2012
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[author] Horn, Roger AR. A. and Johnson, Charles RC. R. (2012). Matrix analysis. Cambridge university press. \endbibitem
2012
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[author] Hsu, DanielD., Kakade, Sham MS. M. and Zhang, TongT. (2012). A spectral algorithm for learning hidden Markov models. Journal of Computer and System Sciences 78 1460–1480. \endbibitem
2012
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[author] Pearson, KarlK. (1894). Contributions to the mathematical theory of evolution. Philosophical Transactions of the Royal Society of London. A 185 71–110. \endbibitem
Cited in the paper.
[author] Negahban, Sahand NS. N., Ravikumar, PradeepP., Wainwright, Martin JM. J. and Yu, BinB. (2012). A unified framework for high-dimensional analysis of M M -estimators with decomposable regularizers. Statistical Science 27 538–557. \endbibitem
2012
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2013
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