MIN Wenwen, LIU Juan, ZHANG Shihua. Sparse Weighted Canonical Correlation Analysis[J]. Chinese Journal of Electronics, 2018, 27(3): 459-466. doi: 10.1049/cje.2017.08.004
Citation: MIN Wenwen, LIU Juan, ZHANG Shihua. Sparse Weighted Canonical Correlation Analysis[J]. Chinese Journal of Electronics, 2018, 27(3): 459-466. doi: 10.1049/cje.2017.08.004

Sparse Weighted Canonical Correlation Analysis

doi: 10.1049/cje.2017.08.004
Funds:  This work is supported by the National Natural Science Foundation of China (No.61422309, No.61379092, No.61621003, No.11661141019), the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (No.XDB13040600), the National Science Foundation of Jiangsu Province (No.BK20161249), the Fundamental Research Funds for the Central Universities (No.2042017KF0233), CAS Frontier Science Research Key Project for Top Young Scientist (No.QYZDBSSW-SYS008), and the Key Laboratory of Random Complex Structures and Data Science, CAS (No.2008DP173182).
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  • Corresponding author: LIU Juan (corresponding author) was born in 1970. She received the Ph.D. degree in computer science from Wuhan University and now serves as a professor and Ph.D. supervisor in Wuhan University. Her research interests include data mining, nature language process and bioinformatics. (Email:liujuan@whu.edu.cn)
  • Received Date: 2016-12-02
  • Rev Recd Date: 2017-06-06
  • Publish Date: 2018-05-10
  • Given two data matrices X and Y, Sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Yv. Classical and sparse Canonical correlation analysis (CCA) models consider the contribution of all the samples of data matrices and thus cannot identify an underlying specific subset of samples. We propose a novel Sparse weighted canonical correlation analysis (SWCCA), where weights are used for regularizing different samples. We solve the L0-regularized SWCCA (L0-SWCCA) using an alternating iterative algorithm. We apply L0-SWCCA to synthetic data and real-world data to demonstrate its effectiveness and superiority compared to related methods. We consider also SWCCA with different penalties like Least absolute shrinkage and selection operator (LASSO) and Group LASSO, and extend it for integrating more than three data matrices.
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  • A. Klami, S. Virtanen and S. Kaski,"Bayesian canonical correlation analysis", J. Mach. Learn. Res., Vol.14, pp.965-1003, 2013.
    L. Sun, S. Ji and J. Ye, "A least squares formulation for canonical correlation analysis", International Conference on Machine Learning (ICML), Helsinki, Finland, pp.1024-1031, 2008.
    X. Yang, W. Liu, D. Tao, et al., "Canonical correlation analysis networks for two-view image recognition", Information Sciences, Vol.385-386, pp.338-352, 2017.
    J. Cai, Y. Tang and J. Wang, "Kernel canonical correlation analysis via gradient descent", Neurocomputing, Vol.182, pp.322-331, 2016.
    C. Wang, J. Liu, W. Min, et al., "A novel sparse penalty for singular value decomposition", Chinese Journal of Electronics, Vol.26, No.2, pp.306-312, 2017.
    D.M. Witten, R.J. Tibshirani and T. Hastie, "A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis", Biostatistics, Vol.10, No.3, pp.515-534, 2009.
    S. Mizutani, E. Pauwels, V. Stoven, et al., "Relating drugprotein interaction network with drug side effects", Bioinformatics, Vol.28, No.18, pp.i522-i528, 2012.
    K.A. Lé Cao, P.G. Martin, C. Robert-Grani, et al., "Sparse canonical methods for biological data integration:Application to a cross-platform study", BMC Bioinformatics, Vol.10, Article ID 34, 17 pages, 2009.
    J. Fang, D. Lin, S.C. Schulz, et al., "Joint sparse canonical correlation analysis for detecting differential imaging genetics modules", Bioinformatics, Vol.32, No.22, pp.3480-3488, 2016.
    Y. Kosuke, Y. Junichiro and D. Kenji, "Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data", BMC Bioinformatics, Vol.18, Article ID 108, 11 pages, 2017.
    E. Parkhomenko, D. Tritchler and J. Beyene, "Sparse canonical correlation analysis with application to genomic data integration", Stat. Appl. Genet. Mol. Biol., Vol.8, No.1, pp.1-34, 2009.
    D.M. Witten and R.J. Tibshirani, "Extensions of sparse canonical correlation analysis with applications to genomic data", Stat. Appl. Genet. Mol. Biol., Vol.10, No.3, pp.515-534, 2009.
    M. Asteris, A. Kyrillidis, O. Koyejo, et al., "A simple and provable algorithm for sparse diagonal CCA", International Conference on Machine Learning (ICML), New York, NY, USA, pp.1148-1157, 2016.
    D. Chu, L.Z. Liao, M.K. Ng, et al., "Sparse canonical correlation analysis:New formulation and algorithm", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.12, pp.3050-3065, 2013.
    D.R. Hardoon and J. Shawe-Taylor, "Sparse canonical correlation analysis", Mach. Learn., Vol.83, No.3, pp.331-353, 2011.
    C. Gao, Z. Ma, Z. Ren, et al., "Minimax estimation in sparse canonical correlation analysis", The Annals of Statistics, Vol.43, No.5, pp.2168-2197, 2015.
    D. Lin, J. Zhang, J. Li, et al., "Group sparse canonical correlation analysis for genomic data integration", BMC Bioinformatics, Vol.14, Article ID 245, 16 pages, 2013.
    S. Virtanen, A. Klami, and S. Kaski, "Bayesian CCA via group sparsity", International Conference on Machine Learning (ICML), Bellevue, WA, USA, pp.457-464, 2011.
    J. Chen, F.D. Bushman, J.D. Lewis, et al., "Structureconstrained sparse canonical correlation analysis with an application to microbiome data analysis", Biostatistics, Vol.14, No.2, pp.244-258, 2013.
    X. Chen, H. Liu, and J.G. Carbonell, "Structured sparse canonical correlation analysis", International Conference on Artificial Intelligence and Statistics (AISTATS), La Palma, Canary Islands, pp.199-207, 2012.
    L. Du, H. Huang, J. Yan, et al., "Structured sparse canonical correlation analysis for brain imaging genetics:An improved GraphNet method", Bioinformatics, Vol.32, No.10, pp.1544-1551, 2016.
    L. Jacob, G. Obozinski and J.P. Vert, "Group lasso with overlap and graph lasso", International Conference on Machine Learning (ICML), Montreal, Canada, pp.433-440, 2009.
    X. Dai, T. Li, Z. Bai, et al., "Breast cancer intrinsic subtype classification, clinical use and future trends", American Journal of Cancer Research, Vol.5, No.10, pp.2929-2943, 2015.
    J. Chen and S. Zhang, "Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data", Bioinformatics, Vol.32, No.11, pp.1724-1732, 2016.
    W. Min, J. Liu, F. Luo, et al., "A novel two-stage method for identifying microRNA-gene regulatory modules in breast cancer", IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington, D.C., USA, pp.151-156, 2015.
    J. Sun, J. Lu, T. Xu et al., "Multi-view sparse co-clustering via proximal alternating linearized minimization", International Conference on Machine Learning (ICML), Lille, France, pp.757-766, 2015
    K. Chin, S. Devries, J. Fridlyand, et al., "Genomic and transcriptional aberrations linked to breast cancer pathophysiologies", Cancer Cell, Vol.10, No.6, pp.529-541, 2006.
    S. Zhang, Q. Li and X.J. Zhou, "A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules", Bioinformatics, Vol.27, No.13, pp.i401-i409, 2011.
    Wiklund E.D. Bramsen, J.B. Bramsen, et al., "Coordinated epigenetic repression of the miR-200 family and miR-205 in invasive bladder cancer", International Journal of Cancer, Vol.27, No.6, pp.1327-1334, 2011.
    Y. Cheng, X. Zhang, P. Li, et al., "Mir-200c promotes bladder cancer cell migration and invasion by directly targeting recK", OncoTargets and Therapy, doi:10.2147/OTT.S101067, Vol.9, pp.5091-5099, 2016.
    P. Tseng, "Convergence of a block coordinate descent method for non-differentiable minimization", Journal of Optimization Theory and Applications, Vol.109, No.3, pp.475-494, 2001.
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