%0 Journal Article
%T Automatically Outlier-Resisting Subspace Learning<br>一种自动抑制离群点的子空间学习方法
%A Pang Yan-wei
%A Liu Zheng-kai
%A <br>庞彦伟
%A 刘政凯
%J 电子与信息学报
%D 2008
%I 
%X Subspace learning is an effective dimensionality reduction method. However, the resulting basis vectors are significantly biased due to the presence of outlier points. Consequently, the transformed data in the subspace cannot faithfully describe the intrinsic distribution of the original data. To tackle this problem, a modified subspace learning algorithm is proposed. In the algorithm it is not necessary to detect outliers. Moreover, the algorithm is reduced to an eignenvalue problem which has a globally optimal solution. Experiments on synthetic data demonstrate the effectiveness of the proposed algorithm.
%K Subsoace learning
%K Dimension reduction
%K Outlier data<br>子空间
%K 降维
%K 离群数据
%K 自动
%K 离群点
%K 子空间学习
%K 降维方法
%K Learning
%K 试验
%K 仿真数据
%K 全局最优解
%K 分解问题
%K 特征值
%K 求解
%K 子空间的
%K 位置
%K 直接探测
%K 改进
%K 离群数据
%K 真实分布
%K 影响
%K outlier
%K 基向量
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=55B981991C645F74D00F2F7F66981AAB&yid=67289AFF6305E306&vid=340AC2BF8E7AB4FD&iid=CA4FD0336C81A37A&sid=5BC9492E1D772407&eid=E114CF9BB47B65BE&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=10