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Data analysis / Singular value decomposition / Linear discriminant analysis / Principal component analysis / Eigenvalues and eigenvectors / Covariance matrix / Support vector machine / Linear regression / Variance / Statistics / Multivariate statistics / Statistical classification


Effective Linear Discriminant Analysis for High Dimensional, Low Sample Size Data Zhihua Qiao,∗ Lan Zhou† and Jianhua Z. Huang‡ Abstract— In the so-called high dimensional, low sample size (HDLSS) settings, LDA p
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Document Date: 2008-02-29 23:01:57

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College Station / Sci / Cambridge / /

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Oxford University Press / Theoretical LDA / regularized LDA / /

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United States / /

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Texas A&M University / US National Cancer Institute / Memorial Drive / Blocker Building / University of Pennsylvania / /

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analytical solution / sparse rLDA algorithm / data mining / numerical algorithm / computation algorithm / supervised dimension reduction tool / /

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Royal Statistical Society / School of Management / Vol / Wharton School / American Statistical Association / University of Pennsylvania / MIT / Department of Statistics / US National Science Foundation / Texas A&M University / Oxford University / US National Cancer Institute / /

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LDA Fisher / Hb / matrix Hb / kRw Hb / Hb Rw Rw HbT +Hb / AB Hb / classical Fisher / Introduction Fisher / HbT Hb / Fisher / /

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Pentax K-x Digital Camera / Wilcoxon / /

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Texas / Pennsylvania / Massachusetts / /

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Journal of the Royal Statistical Society / /

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sparse rLDA algorithm / LAN / DNA Chip / computation algorithm / gene expression / numerical algorithm / data mining / simulation / sparse LDA algorithm / /

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