Unsupervised linear discrimination using skewness
研究了在两组高斯分离问题中，无需标签即可估计最优判别方向的方法，提出了基于偏度的四种无监督估计量，并推导了它们的极限分布，通过模拟验证了结果。
It is well-known that, in Gaussian two-group separation, the optimally discriminating projection direction can be estimated without any knowledge on the group labels. In this work, we gather several such unsupervised estimators based on skewness and derive their limiting distributions. As one of our main results, we show that all affine equivariant estimators of the optimal direction have proportional asymptotic covariance matrices, making their comparison straightforward. Two of our four estimators are novel and two have been proposed already earlier. We use simulations to verify our results and to inspect the finite-sample behaviors of the estimators.