  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.
