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Linear Discriminant Analysis (LDA) on Electronic Health Records (EHR) data iswidely-used for early detection of diseases. Classical LDA for EHR dataclassification, however, suffers from two handicaps: the ill-posed estimationof LDA parameters (e.g., covariance matrix), and the "linear inseparability" ofEHR data. To handle these two issues, in this paper, we propose a novelclassifier FWDA -- Fast Wishart Discriminant Analysis, that makes predictionsin an ensemble way. Specifically, FWDA first surrogates the distribution ofinverse covariance matrices using a Wishart distribution estimated from thetraining data, then "weighted-averages" the classification results of multipleLDA classifiers parameterized by the sampled inverse covariance matrices via aBayesian Voting scheme. The weights for voting are optimally updated to adapteach new input data, so as to enable the nonlinear classification. Theoreticalanalysis indicates that FWDA possesses a fast convergence rate and a robustperformance on high dimensional data. Extensive experiments on large-scale EHRdataset show that our approach outperforms state-of-the-art algorithms by alarge margin.

FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification