Orthogonal discriminant neighborhood analysis for tumor classification

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• 作者：Chuanlei Zhang ; Ying-Ke Lei ; Shanwen Zhang ; Jucheng Yang ; Yihua Hu
• 关键词：Locality sensitive discriminant analysis (LSDA) ; Microarray data ; Orthogonal discriminant neighborhood analysis (ODNA) ; Tumor classification
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：20
• 期：1
• 页码：263-271
• 全文大小：828 KB
• 参考文献：Alok S, Kuldip KP (2008) Cancer classification by gradient LDA technique using microarray microarray data. Data Knowl Eng 66:338–347CrossRef
  Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396CrossRef MATH
  Cai D, He X, Zhou K, Han J, Bao H (2007) Locality sensitive discriminant analysis. In: Proceedings of the twentieth international joint conference on artificial intelligence 2007
  Chen CL, Gong YC, Bie RF (2008) Discriminant analysis methods for microarray data classification. LNAI 5360:268–277
  Dawson K, Rodriguez RL, Maly W (2005) Sample phenotype clusters in high-density oligonucleotide microarray datasets are revealed using Isomap, a nonlinear algorithm. BMC Bioinf 6(1):195CrossRef
  Duchene J, Leclercq S (1988) An optimal transformation for discriminant and principal component analysis. IEEE Trans PAMI 10(6):978–983CrossRef MATH
  Guyon I, Weston J, Barnhill S et al (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389–422CrossRef MATH
  Hu H (2008) Orthogonal neighborhood preserving discriminant analysis for face recognition. Pattern Recognit 41(6):2045–2054
  Hwang T, Sun CH, Yun T et al (2010) FIGS: a filter-based gene selection workbench for microarray data. BMC Bioinf 11:50
  Kruskal W, Wallis W (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 260(1952):583–621
  Pillati M, Viroli C (2005) Supervised locally linear embedding for classification, an application to microarray data analysis. In: Proceedings of 29th annual conference of the of the German classification society, pp 15–18
  Ping X, Brock GN, Parrish RS (2009) Modified linear discriminant analysis approaches for classification of high-dimensional microarray data. Comput Stat Data Anal 53:1674–1687CrossRef MATH
  Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326CrossRef
  Saul LK, Roweis ST (2003) Think globally, fit locally: unsupervised learning of low dimensional manifold. J Mach Learn Res 4:119–155MathSciNet
  Shi C, Chen L (2005) Feature dimension reduction for microarray data analysis using locally linear embedding. In: Proceedings of APBC, pp 211–217
  de Silva V, Tenenbaum J (2003) Global versus local methods in nonlinear dimensionality reduction. Proc Adv Neural Inf Process Syst 15:705–712
  Tanic M, Andress E, Rodriguez-Pinilla SM et al (2013) MicroRNA-based molecular classification of non-BRCA1/2 hereditary breast tumors. Br J Cancer 109(10):2724–2734CrossRef
  Tenenbaum J, de Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323CrossRef
  Wang S-L, Li X-L, Fang J (2012) Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification. BMC Bioinf 13:178CrossRef
  Zhao L, Zhang Z (2009) Supervised locally linear embedding with probability-based distance for classification. Comput Math Appl 57(6):919–926
  Zheng CH, Li B, Zhang L, Wang HQ (2008) Locally linear discriminant embedding for tumor classification. In: Proceedings of ICIC LNAI pp 1093–1100
  
• 作者单位：Chuanlei Zhang (1)
  Ying-Ke Lei (2)
  Shanwen Zhang (3)
  Jucheng Yang (1)
  Yihua Hu (2)
  
  1. School of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin, 300222, China
  2. Electronic Engineering Institute, Hefei, 230027, Anhui, China
  3. Sias International University, Zhengzhou University, Zhengzhou, 451150, China
  
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

An important application of gene expression data is tumor classification. Dimensionality reduction is a key step of tumor classification as gene expression data have the so-called large and small problem. To reduce the dimensionality of the microarray data, in this paper, a novel algorithm called orthogonal discriminant neighborhood analysis (ODNA) is proposed for tumor classification, which can reduce the effect resulting from over-fitting by pre-selecting a small subset of genes. Given a set of data points in the ambient space, a neighbor weight matrix is firstly built to describe the relationship among the data samples. Secondly, optimal between-class scatter matrix and within-class scatter matrix are defined such that the neighborhood structure can be preserved. To improve the discriminating ability, a new method is presented to orthogonalize the basis eigenvectors. The experimental results with two public microarray datasets demonstrate that the proposed ODNA is quite effective and feasible for tumor classification. Keywords Locality sensitive discriminant analysis (LSDA) Microarray data Orthogonal discriminant neighborhood analysis (ODNA) Tumor classification