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Title: Reduced ranked linear discriminant analysis Authors:  Yue Niu - University of Arizona (United States) [presenting]
Ning Hao - University of Arizona (United States)
Bin Dong - Peking University (China)
Abstract: Many high dimensional classification techniques have been developed recently. However, most works focus on only the binary classification problem. Available classification tools for the multi-class cases are either based on over-simplified covariance structure or computationally complicated. Following the idea of reduced ranked linear discriminant analysis, we introduce a new dimension reduction tool with the flavor of supervised principal component analysis. The proposed method is computationally efficient and can incorporate the correlation structure among the features. We illustrate our methods by simulated and real data examples.