%0 Journal Article
%T L1-norm locally linear embedding<br>L1范局部线性嵌入
%A Tao Jianwen
%A Wang Shitong
%A <br>陶剑文
%A 王士同
%J 中国图象图形学报
%D 2011
%I 
%X The problem of dimensionality reduction arises in many fields of information processing, including machine learning, pattern recognition, data mining etc. Locally linear embedding (LLE) is an unsupervised and nonlinear learning algorithm for dimensionality reduction, well-known for its outperformance. Unlike classical LLE, which is based on the L2-norm, a novel L1-norm based LLE (L1-LLE) algorithm is proposed in this article, which is robust to outliers because it utilizes the L1-norm, which is less sensitive to outliers. The proposed L1-norm optimization technique is intuitive, simple, and easy to implement. It is also proven to find a globally minimal solution. The proposed method is applied to several data sets and the performance is compared to those of other conventional methods.
%K dimensionality reduction
%K L1-norm
%K manifold learning
%K locally linear embedding
%K robust<br>降维
%K L1-范数
%K 流形学习
%K 局部线性嵌入
%K 鲁棒性
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=3D120F67FFBE1E37C1EAADF5A3F3391A&yid=9377ED8094509821&vid=7801E6FC5AE9020C&iid=F3090AE9B60B7ED1&sid=0344F3FCD862F7B0&eid=EF31684301C70518&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=1&reference_num=0