Jiang Xudong
School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Link, Singapore.
IEEE Trans Pattern Anal Mach Intell. 2009 May;31(5):931-7. doi: 10.1109/tpami.2008.258.
This paper studies the roles of the principal component and discriminant analyses in the pattern classification and explores their problems with the asymmetric classes and/or the unbalanced training data. An asymmetric principal component analysis (APCA) is proposed to remove the unreliable dimensions more effectively than the conventional PCA. Targeted at the two-class problem, an asymmetric discriminant analysis in the APCA subspace is proposed to regularize the eigenvalue that is, in general, a biased estimate of the variance in the corresponding dimension. These efforts facilitate a reliable and discriminative feature extraction for the asymmetric classes and/or the unbalanced training data. The proposed approach is validated in the experiments by comparing it with the related methods. It consistently achieves the highest classification accuracy among all tested methods in the experiments.
本文研究主成分分析和判别分析在模式分类中的作用，并探讨它们在非对称类和/或不平衡训练数据方面存在的问题。提出了一种非对称主成分分析（APCA），以比传统主成分分析更有效地去除不可靠维度。针对两类问题，在APCA子空间中提出了一种非对称判别分析，以对特征值进行正则化，该特征值通常是相应维度中方差的有偏估计。这些工作有助于为非对称类和/或不平衡训练数据进行可靠且有判别力的特征提取。通过与相关方法进行比较，在实验中验证了所提出的方法。在实验中，它始终在所有测试方法中实现最高的分类准确率。
