论文标题
输入和特征空间中的加权渔民判别分析
Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
论文作者
论文摘要
Fisher判别分析（FDA）是一种子空间学习方法，分别使数据的内部和类间散射最小化。虽然，在FDA中，所有课程对的对待方式相同，但有些课程比其他课程更接近。加权FDA将权重分配给一对班级，以解决FDA的这种缺点。在本文中，我们提出了余弦加权的FDA以及自动加权的自动加权FDA，其中自动发现了权重。我们还建议在功能空间中加权FDA，以建立现有和新提议的权重的加权内核FDA。我们在ORL面部识别数据集上的实验显示了拟议的加权方案的有效性。
Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the pairs of classes are treated the same way, some classes are closer than the others. Weighted FDA assigns weights to the pairs of classes to address this shortcoming of FDA. In this paper, we propose a cosine-weighted FDA as well as an automatically weighted FDA in which weights are found automatically. We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights. Our experiments on the ORL face recognition dataset show the effectiveness of the proposed weighting schemes.
