【学术报告】Reduced Rank Linear Discriminant Analysis

学术报告

报告题目：Reduced Rank Linear Discriminant Analysis

报 告 人：牛玥，亚利桑那大学

时    间：2015年7月03日(星期五)上午10:00

地    点：数学楼第三报告厅

摘    要：Many high dimensional classification techniques have beendeveloped recently. However, most works focus on only the binaryclassification problem. Available classification tools for the multi-classcases are either based on over-simplified covariance structure orcomputationally complicated. In this talk, following the idea of reducedrank linear discriminant analysis, we introduce a new dimensionreduction tool with the flavor of supervised principal component analysis.The proposed method is computationally efficient and can incorporate thecorrelation structure among the features. We illustrate our methods bysimulated and real data examples.

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统计研究院

2015年6月17日