论文标题
关于痕量比方法和Fisher的判别分析，用于鲁棒分类
On the trace ratio method and Fisher's discriminant analysis for robust multigroup classification
论文作者
论文摘要
我们比较了多组分类问题的两个不同的线性降低策略：痕量比方法和费舍尔的判别分析。最近，由于其计算效率以及偶尔更好的分类结果，痕量比率优化已受欢迎。但是，统计理解仍然不完整。我们研究和比较两种方法的属性。然后，我们提出了痕量比方法的强大版本，以处理数据中异常值的存在。我们在污染环境中重新解释了构造与痕量比的渐近扰动。最后，我们使用经典和健壮的估计器比较了痕量比方法和Fisher对合成和真实数据集的判别分析。
We compare two different linear dimensionality reduction strategies for the multigroup classification problem: the trace ratio method and Fisher's discriminant analysis. Recently, trace ratio optimization has gained in popularity due to its computational efficiency, as well as the occasionally better classification results. However, a statistical understanding is still incomplete. We study and compare the properties of the two methods. Then, we propose a robust version of the trace ratio method, to handle the presence of outliers in the data. We reinterpret an asymptotic perturbation bound for the solution to the trace ratio, in a contamination setting. Finally, we compare the performance of the trace ratio method and Fisher's discriminant analysis on both synthetic and real datasets, using classical and robust estimators.
