关键词: 样本加权, 格拉斯曼平均, 主成分分析法, 鲁棒性

Abstract: Grassmann average subspace corresponds to the leading principal component for Gaussian data　　and a scalable principal component analysis is provided. But it assumes that the contribution of each sample is determined by its length, which is sensitive to outliers. For this reason, this paper proposes a novel algorithm based on sample weight and Grassmann average by using local characteristics of data in unsupervised learning or class information in supervised learning.The experimental results on UCI data sets and ORL data set show that the algorithm is more robust and it has improved recognition rate by 1%~2%.

Key words: sample weight, Grassmann average, Principal Component Analysis（PCA）, robust