%0 Journal Article
%T New Ensemble Constructor Based on Locality Preserving Projection for High Dimensional Clustering<br>一种新的基于局部保持投影的高维数据聚类成员构造方法
%A ZHOU Jing-bo
%A YIN Jun
%A JIN Zhong
%A <br>周静波
%A 殷俊
%A 金忠
%J 计算机科学
%D 2011
%I 
%X This paper studied how to construct cluster ensembles for high dimensional data and proposed a new ensemble constructor. ho ameliorate the effect caused by high dimensionality, the proposed method used Locality Preserving Projections(LPP) to reduce the dimensionahty before constructing ensembles. Then constructed ensembles based on random projection combined with K means in LPP subspace. Finally,we discussed how to choose the dimensionality of LPP subspace. hhe experiments show that ensembles generated by new algorithms perform better than those by Princi- pal Component Analysis with subsampling(PCASS) and simple Random Projection(RP) that was proposed before.
%K Cluster cnsembles
%K Dimension reduction
%K I_ocality preserving projections
%K Random projection<br>聚类融合，维度约减，局部保持投影，随机投影
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=A72829DDE955E0D247E452EAFAC46C17&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=9CF7A0430CBB2DFD&sid=6425DAE0271BB751&eid=7EBE588F611589FC&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0