[Formula Omitted]: Lowering the Bound of Misclassification Rate for Sparse Linear Discriminant Analysis via Model Debiasing

Linear discriminant analysis (LDA) is a well-known technique for linear classification, feature extraction, and dimension reduction. To improve the accuracy of LDA under the high dimension low sample size (HDLSS) settings, shrunken estimators, such as Graphical Lasso, can be used to strike a balance...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2019-01, Vol.30 (3), p.707
Hauptverfasser:	Xiong, Haoyi, Cheng, Wei, Bian, Jiang, Hu, Wenqing, Sun, Zeyi, Guo, Zhishan
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Asymptotic properties Classification Convergence Covariance matrix Datasets Discriminant analysis Feature extraction Mathematical models Model accuracy Performance degradation Sparsity Theoretical analysis Upper bounds
Online-Zugang:	Volltext
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