JIANG Wei, MA Tingting, FENG Xiaoting, et al., “Robust Semi-nonnegative Matrix Factorization with Adaptive Graph Regularization for Gene Representation,” Chinese Journal of Electronics, vol. 29, no. 1, pp. 122-131, 2020, doi: 10.1049/cje.2019.11.001
Citation: JIANG Wei, MA Tingting, FENG Xiaoting, et al., “Robust Semi-nonnegative Matrix Factorization with Adaptive Graph Regularization for Gene Representation,” Chinese Journal of Electronics, vol. 29, no. 1, pp. 122-131, 2020, doi: 10.1049/cje.2019.11.001

Robust Semi-nonnegative Matrix Factorization with Adaptive Graph Regularization for Gene Representation

doi: 10.1049/cje.2019.11.001
Funds:  This work is supported by the National Natural Science Foundation of China (No.61771229, No.61672178, No.61702243, No.61702245).
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  • Corresponding author: ZHANG Jie (corresponding author) received the Ph.D. degree in 2015 from the Dalian University of Technology, China. She is currently a lecturer with the School of Mathematics, Liaoning Normal University, China. Her current research interests include geometric processing and machine learning. (Email:jzhang@lnnu.edu.cn)
  • Received Date: 2018-10-17
  • Rev Recd Date: 2019-08-03
  • Publish Date: 2020-01-10
  • Various data representation algorithms have been proposed for gene expression. There are some shortcomings in traditional gene expression methods, such as learning the ideal affinity matrix to effectively capture the geometric structure of genetic data space, and reducing noises and outliers influences of data input. We propose a novel matrix factorization algorithm called Robust semi-nonnegative matrix factorization (RSNMF) with adaptive graph regularization, which simultaneously performs matrix robust factorization with learning affinity matrix in a unified optimization framework. RSNMF also uses a loss function based on l2,1-norm to improve the robustness of the model against noises and outliers. A novel Augmented Lagrange multiplier (ALM) is designed to obtain the optimal solution of RSNMF. The results of extensive experiments that were performed on gene expression datasets demonstrate that RSNMF outperforms the other algorithms, which validates the effectiveness and robustness of RSNMF.
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  • G. Yang and Z. Hu, "Gene feature extraction based on nonnegative dual graph regularized latent low-rank representation", Biomed Research International, Vol.2017, No.12, pp.1-8, 2017.
    D. Wang, J. Liu, Y. Gao, et al., "Characteristic gene selection based on robust graph regularized nonnegative matrix factorization", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol.13, No.6, pp.1059-1067, 2015.
    J. Liu, Y. Xu, C. Zheng, et al., "RPCA-based tumor classification using gene expression data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol.12, No.4, pp.964-970, 2015.
    J. Liu, D. Wang, Y. Gao, et al., "Regularized nonnegative matrix factorization for identifying differential genes and clustering samples:A survey", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol.15, No.3, pp.974-987, 2018.
    L. Dai, C. Feng, J. Liu, et al., "Robust nonnegative matrix factorization via joint graph laplacian and discriminative information for identifying differentially expressed genes", Complexity, Vol.2017, Article ID 4216797, 11 pages, 2017.
    J. Zhang, L. Chen, L. Zhuo, et al., "Multiple saliency features based automatic road extraction from high-resolution multispectral satellite images", Chinese Journal Electronics, Vol.27, No.1, pp.133-139, 2018.
    Z. He, M. Yang and H. Liu, "Multi-task joint feature selection for multi-label classification", Chinese Journal of Electronics, Vol.24, No.2, pp.281-287, 2015.
    Z. Li, J. Tang and X. He, "Robust structured nonnegative matrix factorization for image representation", IEEE Transactions on Neural Networks and Learning Systems, Vol.29, No,5, pp.1947-1960, 2017.
    W. Jiang, M. Li and Y. Zhang, "Neighborhood preserving convex nonnegative matrix factorization", Mathematical Problems in Engineering, Vol.2014, Article ID 154942, 8 pages, 2014.
    W. Zhang, B. Ma, K. Liu, et al., "Video-based pedestrian reidentification by adaptive spatio-temporal appearance model", IEEE Transactions on Image Processing, Vol.26, No.4, pp.2042-2054, 2017.
    C. Yan, H. Xie, S. Liu, et al., "Effective uyghur language text detection in complex background images for traffic prompt identification", IEEE Transactions on Intelligent Transportation Systems, Vol.19, No.1, pp.220-229, 2018.
    R. Luss and A. D'Aspremont, "Clustering and feature selection using sparse principal component analysis", Optimization and Engineering, Vol.11, No.1, pp.145-157, 2010.
    C. Lin, "Projected gradient methods for nonnegative matrix factorization", Neural Computation, Vol.19, No.10, pp.2756-2779, 2007.
    L. Li, G. Lebanon and H. Park, "Fast bregman divergence NMF using taylor expansion and coordinate descent", ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, pp.307-315, 2012.
    L. Xing, H. Dong, W. Jiang, et al., "Nonnegative matrix factorization by joint locality-constrained and l2,1-norm regularization", Multimedia Tools and Applications, Vol.77, No.3, pp.3029-3048, 2018.
    A. Cichocki and P. Anh-Huy, "Fast local algorithms for large scale nonnegative matrix and tensor factorizations", IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol.92, No.3, pp.708-721, 2009.
    W. Jiang, J. Zhang and Y. Zhang, "Concept factorization by Joint locality-constrained and l2,1-norm regularization for image representation", Journal of Multiple-Valued Logic and Soft Computing, Vol.31, No.1, pp.85-103, 2018.
    D. Cai, X. He, J. Han, et al., "Graph regularized nonnegative matrix factorization for data representation", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.33, No.8, pp.1548-1560, 2011.
    D. Cai, X. He and J. Han, "Locally consistent concept factorization for document clustering", IEEE Transactions on Knowledge and Data Engineering, Vol.23, No.6, pp.902-913, 2011.
    J. Ye and Z. Jin, "Graph-regularized local coordinate concept factorization for image representation", Neural Processing Letters, Vol.46, No.2, pp.1-23, 2017.
    W. Zhang, S. Hu, K. Liu, et al., "Motion-free exposure fusion based on inter-consistency and intra-consistency", Information Sciences, Vol.376, pp.190-201, 2017.
    C. Yan, H. Xie, D. Yang, et al., "Supervised hash coding with deep neural network for environment perception of intelligent vehicles", IEEE Transactions on Intelligent Transportation Systems, Vol.19, No.1, pp.284-295, 2018.
    F. Shang, L. Jiao and F. Wang, "Graph dual regularization non-negative matrix factorization for co-clustering", Pattern Recognition, Vol.45, No.6, pp.2237-2250, 2012.
    L. Du and Y. D. Shen, "Towards robust co-clustering", Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing, China, pp.1317-1322, 2013.
    C. Ding, T. Li and M. I. Jordan, "Convex and seminonnegative matrix factorizations", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.32, No.1, pp.45-55, 2009.
    P. Luo and J. Peng, "Group sparsity and graph regularized semi-nonnegative matrix factorization with discriminability for data representation", Entropy, Vol.19, No.12, pp. 627-637, 2017.
    M. Yin, J. Gao and Z. Lin, "Laplacian regularized low-rank representation and its applications", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.38, No.3, pp.504-517, 2016.
    H. Tao, C. Hou, F. Nie, et al., "Scalable multiview semi-supervised classification via adaptive regression", IEEE Transactions on Image Processing, Vol.26, No.9, pp.4283-4296, 2017.
    W. Zhang, L. Ma, L. Ma, et al., "Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network", Pattern Recognition, Vol.59, No.10, pp.176-187, 2016.
    Y. Cao, H. Qi, W. Zhou, et al., "Binary hashing for approximate nearest neighbor search on big data:A survey", IEEE Access, Vol.6, pp.2039-2054, 2018.
    H. Qi, K. Li, Y.Shen, et al., "Object-based image retrieval with kernel on adjacency matrix and local combined features", Acm Transactions on Multimedia Computing Communications and Applications, Vol.8, No.4, pp. 1-18, 2012.
    F. Nie, Z. Hu and X. Li, "Matrix completion based on non-convex low-rank approximation", IEEE Transactions on Image Processing, Vol.28, No.5, pp.2378-2388, 2019.
    W. Zhang, Y. Zhang, L. Ma, et al., "Multimodal learning for facial expression recognition", Pattern Recognition, Vol.48, No.10, pp.3191-3202, 2015.
    F. Nie, H. Zhang, R. Zhang, et al., "Robust multiple rank-k bilinear projections for unsupervised learning", IEEE Trans. on Image Processing, Vol.28, No.5, pp.2574-2583, 2019.
    C. Lu, J. Feng, Z. Lin, et al., "Subspace clustering by block diagonal representation", IEEE Transaction Pattern Analysis and Machine Intelligence, Vol.41, No.2, pp. 487-501, 2019.
    L. Zhuang, J. Wang and S. Gao, " Constructing a nonnegative low-rank and sparse graph with data-adaptive features", IEEE Trans. Image Processing, Vol.24, No.12, pp.4918-4933, 2015.
    C. Lu, J. Feng, S. Yan, et al., "A unified alternating direction method of multipliers by majorization minimization ", IEEE Transaction Pattern Analysis and Machine Intelligence, Vol.40, No.3, pp.527-541, 2018.
    J. Huang, F. Nie, H. Huang, et al., " Robust manifold nonnegative matrix factorization", Journal ACM Transactions on Knowledge Discovery from Data, Vol.8, No.3, pp.1-21, 2014.
    J. Wang, F. Tian, C. Liu, et al., " Robust semisupervised nonnegative matrix factorization", International Joint Conference on Neural Networks, 2015.
    F. Nie, X. Wang, M. Jordan, et al., "The constrained laplacian rank algorithm for graph-based clustering", Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, American, pp.1969-1976, 2016.
    D. Kong, C. Ding and H. Huang, "Robust nonnegative matrix factorization using l2,1-norm", ACM International Conference on Information and Knowledge Management, Glasgow, Scotland, UK, pp.673-682, 2011.
    H. Zhang, Z. Zha, S. Yan, et al., "Robust non-negative graph embedding:towards noisy data, unreliable graphs, and noisy labels", IEEE Int. Conf. on Computer Vision and Pattern Recognition, Rhode Island, USA, pp.2464-2471, 2012.
    S. Zhu, D. Wang, K. Yu, et al., "Feature selection for gene expression using model-based entropy", IEEE/ACM Transactions on Computational Biology Bioinformatics, Vol.7, No.1, pp.25-36, 2010.
    J. Ye, T. Li, T. Xiong, et al., "Using uncorrelated discriminant analysis for tissue classification with gene expression data", IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol.1, No.4, pp.181-190, 2004.
    W. Jiang, J. Liu, H. Qi, et al., "Robust subspace segmentation via nonconvex low rank representation", Information Sciences, Vol.340-341, No.4, pp.144-158, 2016.
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