[1]简彩仁,陈晓云.局部和稀疏保持无监督特征选择法[J].华侨大学学报(自然科学版),2015,36(1):111-115.[doi:10.11830/ISSN.1000-5013.2015.01.0111]
 JIAN Cai-ren,CHEN Xiao-yun.Unsupervised Feature Selection Using Locality and Sparsity Preserving[J].Journal of Huaqiao University(Natural Science),2015,36(1):111-115.[doi:10.11830/ISSN.1000-5013.2015.01.0111]
点击复制

局部和稀疏保持无监督特征选择法()
分享到:

《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第36卷
期数:
2015年第1期
页码:
111-115
栏目:
出版日期:
2015-01-20

文章信息/Info

Title:
Unsupervised Feature Selection Using Locality and Sparsity Preserving
文章编号:
1000-5013(2015)01-0111-05
作者:
简彩仁 陈晓云
福州大学 数学与计算机科学学院, 福建 福州 350116
Author(s):
JIAN Cai-ren CHEN Xiao-yun
College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
关键词:
局部保持投影 稀疏保持投影 高维小样本 无监督 特征选择 聚类
Keywords:
locality preserving projection sparsity preserving projection high-dimensionality small sample unsupervised feature selection clustering
分类号:
TP311;TP371
DOI:
10.11830/ISSN.1000-5013.2015.01.0111
文献标志码:
A
摘要:
利用局部保持投影和稀疏保持投影来刻画数据的本质结构,结合L2,1范数的组稀疏性来选择特征,提出一种新的针对高维小样本数据集的无监督特征选择算法.实验表明:局部和稀疏保持无监督特征选择法是一种有效的无监督特征选择方法;平衡参数对实验结果有较大的影响.
Abstract:
By locality preserving projection and sparsity preserving projection to represent the intrinsic geometrical structure of the data set and use the group sparse of L2,1 norm, one new unsupervised feature selection method for high-dimensionality small sample data set is proposed. Experimental results show that the method is effective and sensitive to balance parameter.

参考文献/References:

[1] 徐峻岭,周毓明,陈林,等.基于互信息的无监督特征选择[J].计算机研究与发展,2012,49(2):372-382.
[2] 张莉,孙钢,郭军.基于 K-均值聚类的无监督的特征选择方法[J].计算机应用研究,2005,22(3):23-24.
[3] HE Xiao-fei,CAI Deng,NIYOGI P.Laplacian score for feature selection[C]//Advances in Neural Information Processing Systems.Vancouver:[s.n.],2005:507-514.
[4] CAI Deng,ZHANG Chi-yuan,HE Xiao-fei.Unsupervised feature selection for multi-cluster data[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Washington DC:ACM,2010:333-342.
[5] YANG Shi-zhun,HOU Chen-ping,NIE Fei-ping,et al.Unsupervised maximum margin feature selection via L2,1-norm minimization[J].Neural Computing and Applications,2012,21(7):1791-1799.
[6] FANG Xiao-zhao,XU Yong,LI Xue-long,et al.Locality and similarity preserving embedding for feature selection[J].Neurocomputing,2014,128:304-315.
[7] GU Quan-quan,LI Zhen-hui,HAN Jia-wei.Joint feature selection and subspace learning[C]//The 22nd International Joint Conference on Artificial Intelligence.Barcelona:[s.n.],2011:1294-1299.
[8] HE Xiao-hui,NIYOGI P.Locality preserving projections[C]//Proceedings of the 17th Annual Conference on Neural Information Processing Systems.Columbia:[s.n.],2003:153-160.
[9] QIAO Li-shan,CHEN Song-can,TAN Xiao-yang.Sparsity preserving projections with applications to face recognition[J].Pattern Recognition,2010,43(1):331-341.
[10] CAI Deng,HE Xiao-fei,WU Xiao-yun,et al.Non-negative matrix factorization on manifold[C]//Proceedings of International Conference on Data Mining.Pisa:IEEE Press,2008:63-72.

备注/Memo

备注/Memo:
收稿日期: 2014-07-01
通信作者: 陈晓云(1970-),女,教授,博士,主要从事数据挖掘、模式识别的研究.E-mail:c_xiaoyun@21cn.com.
基金项目: 国家自然科学基金资助项目(11301084); 福建省自然科学基金资助项目(2014J01009)
更新日期/Last Update: 2015-01-20