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稀疏判别分析 被引量：2

Sparse discriminant analysis
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摘要 针对流形嵌入降维方法中在高维空间构建近邻图无益于后续工作,以及不容易给近邻大小和热核参数赋合适值的问题,提出一种稀疏判别分析算法(SEDA)。首先使用稀疏表示构建稀疏图保持数据的全局信息和几何结构,以克服流形嵌入方法的不足;其次,将稀疏保持作为正则化项使用Fisher判别准则,能够得到最优的投影。在一组高维数据集上的实验结果表明,SEDA是非常有效的半监督降维方法。 Methods for manifold embedding have the following issues： on one hand,neighborhood graph is constructed in such high-dimensionality of original space that it tends to work poorly;on the other hand,appropriate values for the neighborhood size and heat kernel parameter involved in graph construction are generally difficult to be assigned.To address these problems,a new semi-supervised dimensionality reduction algorithm called SparsE Discriminant Analysis（SEDA） was proposed.Firstly,SEDA set up a sparse graph to preserve the global information and geometric structure of the data based on sparse representation.Secondly,it applied both sparse graph and Fisher criterion to seek the optimal projection.The experimental results on a broad range of data sets show that SEDA is superior to many popular dimensionality reduction methods.
作者 陈小冬 林焕祥
机构地区 浙江广播电视大学信息与工程学院 浙江科技学院信息与电子工程学院
出处 《计算机应用》 CSCD 北大核心 2012年第4期1017-1021,共5页 journal of Computer Applications
基金 浙江省自然科学基金资助项目(Y1100349) 浙江省教育厅2011年度科研计划项目(Y201119679) 中央广播电视大学资助项目(GFQ1601) 浙江广播电视大学资助项目(XKT-11J03) 2010年浙江省高校优秀青年教师资助计划项目
关键词 判别分析 稀疏表示 近邻图 稀疏图 discriminant analysis sparse representation neighborhood graph sparse graph
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献27

  • 1YE J,ZHAO Z,WU M.Discriminative K-Means for clustering[EB/OL].[2011-05-01].http://www.kyb.mpg.de/publica-tions/attachments/NIPS2007-Ye_4710[0].pdf.
  • 2CHEN H T,CHANG H W,LIU T L.Local discriminant embeddingand its variants[C]//CVPR'05:Proceedings of the 2005 IEEEComputer Society Conference on Computer Vision and Pattern Rec-ognition.Washington,DC:IEEE Computer Society,2005:846-853.
  • 3尹学松,胡思良,陈松灿.基于成对约束的判别型半监督聚类分析[J].软件学报,2008,19(11):2791-2802. 被引量：52
  • 4QIAO L,CHEN S,TAN X.Sparsity preserving projections with ap-plications to face recognition[J].Pattern Recognition,2010,43(1):331-341.
  • 5Xuesong Yin (12) yinxs@nuaa.edu.cn Enliang Hu (1).Distance metric learning guided adaptive subspace semi-supervised clustering[J].Frontiers of Computer Science,2011,5(1):100-108. 被引量：1
  • 6HOI S C H,LIU W,LYU M R,et al.Learning distance metricswith contextual constraints for image retrieval[C]//CVPR'06:Pro-ceedings of the 2006 IEEE Computer Society Conference on Comput-er Vision and Pattern Recognition.Washington,DC:IEEE Comput-er Society,2006:2072-2078.
  • 7陈小冬,尹学松,林焕祥.基于判别分析的半监督聚类方法[J].计算机工程与应用,2010,46(6):139-143. 被引量：3
  • 8YAN S,XU D,ZHANG B,et al.Graph embedding and exten-sions:A general framework for dimensionality reduction[EB/OL].[2011-08-01].http://www.ntu.edu.sg/home/dongxu/TPAMI-GE.pdf.
  • 9HE XIAO-FEI,NIYOGI P.Locality preserving projections[EB/OL].[2011-08-01].http://people.cs.uchicago.edu/～xi-aofei/conference-24.pdf.
  • 10CAI D,HE X,HAN J.Semi-supervised discriminant analysis[EB/OL].[2011-08-01].http://www.cs.uiuc.edu/～hanj/pdf/ic-cv07_dengcai_SDA.pdf.

二级参考文献83

  • 1Basu S, Banerjee A, Mooney RJ. A probabilistic framework for semi-supervised clustering. In: Boulicaut JF, Esposito F, Giannotti F, Pedreschi D, eds. Proc. of the 10th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York: ACM Press, 2004.59-68.
  • 2Bilenko M, Basu S, Mooney RJ. Integrating constraints and metric learning in semi-supervised clustering. In: Brodley CE, ed. Proc. of the 21st Int'l Conf. on Machine Learning. New York: ACM Press, 2004. 81-88.
  • 3Tang W, Xiong H, Zhong S, Wu J. Enhancing semi-supervised clustering: a feature projection perspective. In: Berkhin P, Caruana R, Wu XD, eds. Proc. of the 13th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York: ACM Press, 2007. 707-716.
  • 4Basu S, Banerjee A, Mooney RJ. Active semi-supervision for pairwise constrained clustering. In: Jonker W, Petkovic M, eds. Proc. of the SIAM Int'l Conf. on Data Mining. Cambridge: MIT Press, 2004. 333-344.
  • 5Yan B, Domeniconi C. An adaptive kernel method for semi-supervised clustering. In: Fiirnkranz J, Scheffer T, Spiliopoulou M, eds. Proc. of the 17th European Conf. on Machine Learning. Berlin: Sigma Press, 2006. 18-22.
  • 6Yeung DY, Chang H. Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints. Pattern Recognition, 2006,39(5):1007-1010.
  • 7Beyer K, Goldstein J, Ramakrishnan R, Shaft U. When is "Nearest Neighbors Meaningful"? In: Beeri C, Buneman P, eds. Proc. of the Int'l Conf. on Database Theory. New York: ACM Press, 1999.217-235.
  • 8Ding CH, Li T. Adaptive dimension reduction using discriminant analysis and K-means clustering. In: Ghahramani Z, ed. Proc. of the 19th Int'l Conf. on Machine Learning. New York: ACM Press, 2007.521-528.
  • 9Zhang DQ, Zhou ZH, Chen SC. Semi-Supervised dimensionality reduction. In: Mandoiu I, Zelikovsky A, eds. Proc. of the 7th SIAM Int'l Conf. on Data Mining. Cambridge: MIT Press, 2007. 629-634.
  • 10Ye JP, Zhao Z, Liu H. Adaptive distance metric learning for clustering. In: Bishop CM, Frey B, eds. Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. Madison: IEEE Computer Society Press, 2007. 1-7.

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计算机应用
2012年 第4期

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