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B1378
Title: High dimensional discriminant analysis for structurally dependent data Authors:  Taps Maiti - Michigan State University (United States) [presenting]
Yingjie Li - Michigan State University (United States)
Abstract: Linear discriminant analysis (LDA) is one of the most classical and popular classification techniques. However, it performs poorly in high-dimensional classification. Many sparse discriminant methods have been proposed to make LDA applicable in high dimensional case. One issue of those methods is the structure of the covariance among features is ignored. We propose a new procedure for high dimensional discriminant analysis for structurally correlated data. Specifically, we will discuss spatially structured data. Penalized maximum likelihood estimation (PMLE) is developed for feature selection and parameter estimation. Tapering technique is applied to reduce computation load. The theory shows that the method proposed can achieve consistent parameter estimation, features selection, and asymptotically optimal misclassification rate. Extensive simulation study shows a significant improvement in classification performance under spatial dependence.