We consider the linear discriminant analysis problem in the high-dimensional settings. In this work, we propose PANDA(PivotAl liNear Discriminant Analysis), a tuning-insensitive method in the sense that it requires very little effort to tune the parameters. Moreover, we prove that PANDA achieves the optimal convergence rate in terms of both the estimation error and misclassification rate. Our theoretical results are backed up by thorough numerical studies using both simulated and real datasets. In comparison with the existing methods, we observe that our proposed PANDA yields equal or better performance, and requires substantially less effort in parameter tuning.

翻译：本文考虑高维设定下的线性判别分析问题。我们提出PANDA（枢轴线性判别分析），这是一种对调参不敏感的方法，即该方法几乎不需要调整参数。此外，我们证明了PANDA在估计误差和误分类率方面均达到了最优收敛速率。通过使用模拟数据集和真实数据集进行的全面数值研究，我们的理论结果得到了验证。与现有方法相比，我们观察到所提出的PANDA取得了相同或更好的性能，且参数调优所需的工作量显著减少。