|Table of Contents|

Semi-supervised feature selection based on structure and constraints preserving

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2014年04期
Page:
518-525
Research Field:
Publishing date:

Info

Title:
Semi-supervised feature selection based on structure and constraints preserving
Author(s):
Pan Jun1Wang Ruiqin2Kong Fansheng3
1.Institute of Information Security; 2.College of Physics and Electronic Information Engineering, Wenzhou University,Wenzhou 325035,China; 3.College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China
Keywords:
feature selection semi-supervised learning pairwise constrains structure and constraints preserving feature ranking geometrical structure supervision information
PACS:
TP391
DOI:
-
Abstract:
To overcome the deficiency of most existing feature selection methods which fairly respect both the geometrical structure and the supervision information,a novel approach called semi-supervised feature selection based on structure and constraints preserving is proposed.In this method,both the pairwise constraints and the local and nonlocal structure are taken into account,and a new feature selection criterion,i.e.structure and constraints preserving(SCP)score is defined.The SCP score exploites abundant unlabeled data points to learn the geometrical structure of the data space,and uses a few pairwise constraints to discover the margins of different classes.Those features that can preserve the geometrical structure and pairwise constraints information are selected.Experimental results from several datasets show that the proposed method achieves better performance than the feature ranking selection methods.

References:

[1] 杨静宇,金忠,郭跃飞.人脸图像有效鉴别特征抽取与识别[J].南京理工大学学报,2000,24(3):193-198.
Yang Jingyu,Jin Zhong,Guo Yuefei.Efficient discriminant feature extraction and recognition of face images[J].Journal of Nanjing University of Science and Technology,2000,24(3):193-198.
[2]姚旭,王晓丹,张玉玺,等.特征选择方法综述[J].控制与决策,2012,27(2):161-166.
Yao Xu,Wang Xiaodan,Zhang Yuxi,et al.Summary of feature selection algorithms[J].Control and Decision,2012,27(2):161-166.
[3]Li Yun,Lu Baoliang.Feature selection based on loss-margin of nearest neighbor classification[J].Pattern Recognition,2009,42(8):1921-1941.
[4]Zhao Jidong,Lu Ke,He Xiaofei.Locality sensitive semi-supervised feature selection[J].Neurocomputing,2008(71):1842-1849.
[5]蔡哲元,余建国,李先鹏,等.基于核空间距离测度的特征选择[J].模式识别与人工智能,2010,23(2):235-240.
Cai Zheyuan,Yu Jianguo,Li Xianpeng,et al.Feature selection algorithm based on kernel distance measure[J].Pattern Recognition and Artificial Intelligence,2010,23(2):235-240.
[6]业巧林,赵春霞,陈小波.基于正则化技术的对支持向量机特征选择算法[J].计算机研究与发展,2011,48(6):1029-1037.
Ye Qiaolin,Zhao Chunxia,Chen Xiaobo.A feature selection method for twsvm via a regularization technique[J].Journal of Computer Research and Development,2011,48(6):1029-1037.
[7]薛晖,陈松灿.基于局部性正则化推广误差界的特征选择算法[J].模式识别与人工智能,2011,24(4):474-478.
Xue Hui,Chen Songcan.Feature selection based on locality regularized generalization error bound[J].Pattern Recognition and Artificial Intelligence,2011,24(4):474-478.
[8]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.
Xu Junling,Zhou Yuming,Chen Lin,et al.An unsupervised feature selection approach based on mutual information[J].Journal of Computer Research and Development,2012,49(2):372-382.
[9]Bishop C M.Neural networks for pattern recognition[M].Oxford,UK:Oxford University Press,1995.
[10]He Xiaofei,Cai Deng,Niyoi P.Laplacian score for feature selection[A].Adv Neural Inf Proces Sys[C].Vancouver,Canada:NIPS Foundation,2005:507-514.
[11]Zhang Daoqiang,Chen Songcan,Zhou Zhihua.Constraint score:a new filter method for feature selection with pairwise constraints[J].Pattern Recognition,2008,41(5):1440-1451.
[12]Sun Dan,Zhang Daoqiang.Bagging constraint score for feature selection with pairwise constraints[J].Pattern Recognition,2010,43(6):2106-2118.
[13]He Xiaofei,Yan Shuicheng,Hu Yuxiao,et al.Face recognition using laplacianfaces[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3):328-340.
[14]Yang Jian,Zhang Daoqiang,Yang Jingyu,et al.Globally maximizing,locally minimizing:unsupervised discriminant projection with applications to face and palm biometrics[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(4):650-664.
[15]严云洋,郭志波,陈伏兵,等.融合多尺度多特征的人脸识别方法[J].南京理工大学学报,2009,33(1):48-52.
Yan Yunyang,Guo Zhibo,Chen Fubing,et al.Face recognition based on fusion of multi features with multi scales[J].Journal of Nanjing University of Science and Technology,2009,33(1):48-52.
[16]Chung F R K.Spectral graph theory[M].Fresno,USA:American Mathematical Society,1997.
[17]Blake C L,Merz C J.UCI repository of machine learning databases[EB/OL].[2011-12-26].http://archive.ics.uci.edu/ml.
[18]Samaria F S,Harter A C.Parameterization of a stochastic model for human face identification[A].IEEE Workshop Appl Comput Vision[C].Sarasota,USA:IEEE,Los Alamitos,1994:138-142.
[19]Georghiades A S,Belhumeur P N,Kriegman D J.From few to many:illumination cone models for face recognition under variable lighting and pose[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(6):643-660.

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Last Update: 2014-08-31