Prediction and estimation consistency of sparse multi-class penalized optimal scoring
Prediction and estimation consistency of sparse multi-class penalized optimal scoring
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DOI：
10.3150/19-bej1126
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发表时间：
2018-09
期刊：
影响因子：
1.5
通讯作者：
Irina Gaynanova
中科院分区：
文献类型：
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作者：
Irina Gaynanova
稀疏线性判别分析通过惩罚最佳评分是在高维设置中的成功分类工具。尽管已经建立了稀疏最佳评分的变量选择一致性，但缺乏相应的预测和估计一致性结果。我们通过在样本外预测误差和多级惩罚最佳评分的估计误差上提供概率界限来弥合此差距，从而允许分流数的类数量。
Sparse linear discriminant analysis via penalized optimal scoring is a successful tool for classification in high-dimensional settings. While the variable selection consistency of sparse optimal scoring has been established, the corresponding prediction and estimation consistency results have been lacking. We bridge this gap by providing probabilistic bounds on out-of-sample prediction error and estimation error of multi-class penalized optimal scoring allowing for diverging number of classes.