%0 Journal Article
%T A Localized Linear Manifold Self-organizing Map<br>一种局部化的线性流形自组织映射
%A ZHENG Hui-Cheng
%A SHEN Wei
%A <br>郑慧诚
%A 沈伟
%J 自动化学报
%D 2008
%I 
%X This paper presents a method of localized linear manifold self-organizing map,which is able to learn a set of ordered low-dimensional linear manifolds in the high-dimensional vector space.Compared to state-of-the-art methods based on Kohonen's adaptive-subspace self-organizing map (ASSOM),our method avoids confusion of data in the manifold representation.Each neuron in the network approximately learns the mean vector and principal subspace of the data in its local region.The data representation is therefore more discernable.Experiments show that the proposed method performs much better than other three methods in separating clusters.In terms of handwritten digit recognition,the proposed method achieves an accuracy of 98.26 % on the training set of the MNIST database and 97.46 % on the test set.
%K Self-organizing map (SOM)
%K manifold learning
%K handwritten digit recognition<br>自组织映射
%K 流形学习
%K 手写体数字识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=7F9B233255D82A42826424942089A2B5&yid=67289AFF6305E306&vid=339D79302DF62549&iid=F3090AE9B60B7ED1&sid=61548B9F608D3CE3&eid=615D9FEA4E7369ED&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=14