$\mathcal{DBSDA}$ : Lowering the Bound of Misclassification Rate for Sparse Linear Discriminant Analysis via Model Debiasing

IEEE Transactions on Neural Networks and Learning Systems, pp. 707-717, 2019.

Cited by: 0|Bibtex|Views46|DOI:https://doi.org/10.1109/TNNLS.2018.2846783
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Abstract:

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 between biases and variances. Although ...More

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