The Annals of Applied Statistics

Feature selection in omics prediction problems using cat scores and false nondiscovery rate control

Miika Ahdesmäki and Korbinian Strimmer

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We revisit the problem of feature selection in linear discriminant analysis (LDA), that is, when features are correlated. First, we introduce a pooled centroids formulation of the multiclass LDA predictor function, in which the relative weights of Mahalanobis-transformed predictors are given by correlation-adjusted t-scores (cat scores). Second, for feature selection we propose thresholding cat scores by controlling false nondiscovery rates (FNDR). Third, training of the classifier is based on James–Stein shrinkage estimates of correlations and variances, where regularization parameters are chosen analytically without resampling. Overall, this results in an effective and computationally inexpensive framework for high-dimensional prediction with natural feature selection. The proposed shrinkage discriminant procedures are implemented in the R package “sda” available from the R repository CRAN.

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Ann. Appl. Stat. Volume 4, Number 1 (2010), 503-519.

First available in Project Euclid: 11 May 2010

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Feature selection linear discriminant analysis correlation James–Stein estimator “small n, large p” setting correlation-adjusted t-score false discovery rates higher criticism


Ahdesmäki, Miika; Strimmer, Korbinian. Feature selection in omics prediction problems using cat scores and false nondiscovery rate control. Ann. Appl. Stat. 4 (2010), no. 1, 503--519. doi:10.1214/09-AOAS277.

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