标题： 高维小样本数据的鲁棒分类 显示英文标题

标题： Robust Classification of High Dimension Low Sample Size Data

摘要： 近年来，模式识别技术的鲁棒性增强一直是研究的热点。 尽管关于该主题已有大量论文发表，但很少有文章深入探讨高维小样本背景下鲁棒分类这一课题。 在这项工作中，我们探索并比较了鲁棒分类技术的预测性能，特别关注鲁棒判别分析和鲁棒主成分分析（PCA）在多种大型$p$小型$n$数据集上的应用。 此外，我们通过对比单模型方法与集成方法在这一背景下的差异，研究了随机森林的性能表现。 我们的研究表明，虽然随机森林并非专门设计来抵抗异常值，但它在现有专门设计用于实现鲁棒性的技术中表现出显著优越的预测性能。 事实上，在真实数据和模拟数据上，随机森林都展现出最佳的预测能力。 显示英文摘要

摘要： The robustification of pattern recognition techniques has been the subject of intense research in recent years. Despite the multiplicity of papers on the subject, very few articles have deeply explored the topic of robust classification in the high dimension low sample size context. In this work, we explore and compare the predictive performances of robust classification techniques with a special concentration on robust discriminant analysis and robust PCA applied to a wide variety of large $p$ small $n$ data sets. We also explore the performance of random forest by way of comparing and contrasting the differences single model methods and ensemble methods in this context. Our work reveals that Random Forest, although not inherently designed to be robust to outliers, substantially outperforms the existing techniques specifically designed to achieve robustness. Indeed, random forest emerges as the best predictively on both real life and simulated data.