WANG Nian, WANG Junsheng, GE Fang, “Gene Expression Data Classification Using Laplacian Eigenmap Based on Improved Maximum Margin Criterion,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 521-524, 2013,
Citation: WANG Nian, WANG Junsheng, GE Fang, “Gene Expression Data Classification Using Laplacian Eigenmap Based on Improved Maximum Margin Criterion,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 521-524, 2013,

Gene Expression Data Classification Using Laplacian Eigenmap Based on Improved Maximum Margin Criterion

Funds:  This work is supported by the National Natural Science Foundation of China (No.61172127), Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20113401110006), Natural Science Foundation of Anhui Province (No.1208085MF93, No.1208085QF104) and Innovative research team of 211 project in Anhui University (No.KJTD007A).
  • Received Date: 2012-01-01
  • Rev Recd Date: 2012-11-01
  • Publish Date: 2013-06-15
  • To deal with the insufficiency problem of Laplacian eigenmap (LE) method and Maximum margin criterion (MMC) method in feature extraction, a new dimensionality reduction method called Laplacian eigenmap based on Improved maximum margin criterion (LE/IMMC) is proposed with applications in gene expression data classification. The LE/IMMC intends to constrain similar data points as close to each other as possible and maximize the margin regions between different pattern classes simultaneously. The proposed LE/IMMC by introducing IMMC into the cost function of LE retains the characteristic of local neighborhood relationship of LE. Meanwhile, it emphasizes the discriminative information by incorporating IMMC, which can maximize the betweenclass scatter and minimize the within-class scatter. Gene expression data classification experiments on four public datasets demonstrate our method is effective for feature extraction.
  • loading
  • G. George, V. Raj, “Review on feature selection techniques and the impact of SVM for cancer classification using gene expression profile”, International Journal of Computer Science & Engineering Survey, Vol.2, No.3, pp.42-53, 2011.
    X. Wang, Q. Liu, Y. Cheng, “Missing value estimation for gene expression profile data”, Chinese Journal of Electronics, Vol.21, No.4, pp.673-677, 2012.
    X. Wang, Q. Liu, Y. Cheng et al., “Qualitative analysis of gene regulatory networks based on angular discretization”, Chinese Journal of Electronics, Vol.20, No.4, pp.646-650, 2011.
    Y. Guo, T. Hastie, R. Tibshirani, “Regularized linear discriminant analysis and its application in microarrays”, Biostatistics, Vol.8, No.1, pp.86-100, 2007.
    H. Li, T. Jiang, K. Zhang, “Efficient and robust feature extraction by maximum margin criterion”, IEEE Transactions on Neural Networks, Vol.17, No.1, pp.1157-1165, 2006.
    W. Yang, J. Wang, M. Ren et al., “Feature extraction based on Laplacian bidirectional maximum margin criterion”, Pattern Recognition, Vol.42, No.11, pp.2327-2334, 2009.
    M. Belkin, P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation”, Neural Computation, Vol.15, No.6, pp.1373-1396, 2003.
    S.T. Roweis, L.K. Saul, “Nonlinear dimensionality reduction by locally linear embedding”, Science, Vol.290, No.5500, pp.23232326, 2000.
    T. Lin, H. Zha, “Riemannian manifold learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.30, No.5, pp.796-809, 2008.
    C. Chen, L. Zhang, J. Bu et al., “Constrained Laplacian eigenmap for dimensionality reduction”, Neurocomputing, Vol.73, No.4, pp.951-958, 2010.
    M. Lewandowski et al., “Temporal extension of Laplacian eigenmaps for unsupervised dimensionality reduction of time series”, International Conference on Pattern Recognition (ICPR), London, UK, pp.161-164, 2010.
    D. Singh, P.G. Febbo, K. Ross et al., “Gene expression correlates of clinical prostate cancer behavior”, Cancer Cell, Vol.1, No.2, pp.203-209, 2002.
    M. Pillati, C. Viroli, “Supervised locally linear embedding for classification: An application to gene expression data analysis”, Meeting of the classification and data analysis group of the Italian Statistical Society, Parma, Italy, pp.147-150, 2005.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (535) PDF downloads(1107) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return