一种自适应最大拒绝鉴别分析及其鉴别矢量集

针对 MRC-Boosting方法中的弱分类器二值化以及鉴别矢量不正交等问题，提出一种自适应最大拒绝鉴别分析(AdaMRDA)，进一步提高分类性能。通过已抽取的鉴别特征到期望中心的距离，设计一种自适应权重调整方法，使得后面得到的鉴别矢量更加有利于分类，并且给出最佳正交鉴别矢量集的求解方程。最后，通过在2个数据库上的实验证明，AdaMRDA方法在分类性能上明显优于MRC-Boosting方法及相关方法。

Adaptive Maximal Rejection Discriminant Analysis and Its Discriminant Vectors

Aiming at the two-value output of weak classifiers and Non-orthogonal discriminant vectors on the MRC-Boosting, an adaptive maximal rejection discriminant analysis (AdaMRDA) is proposed to further improve the classification performance. Based on the Euclid distance between the extracted discriminant features and their expectation mean, an adaptive updating weights method is developed firstly, by which the latter discriminant vectors obtained are more beneficial for classification. Then the equation solving the optimal orthogonal discriminant vectors is given. Finally, the experimental results on 2 databases prove that AdaMRDA is superior to MRC-Boosting and related methods on classification performance.