[1]徐慧敏,陈秀宏.图正则化稀疏判别非负矩阵分解[J].智能系统学报,2019,14(6):1217-1224.[doi:10.11992/tis.201811021]
XU Huimin,CHEN Xiuhong.Graph-regularized, sparse discriminant, non-negative matrix factorization[J].CAAI Transactions on Intelligent Systems,2019,14(6):1217-1224.[doi:10.11992/tis.201811021]
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XU Huimin,CHEN Xiuhong.Graph-regularized, sparse discriminant, non-negative matrix factorization[J].CAAI Transactions on Intelligent Systems,2019,14(6):1217-1224.[doi:10.11992/tis.201811021]
图正则化稀疏判别非负矩阵分解
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第6期
页码:
1217-1224
栏目:
学术论文—人工智能基础
出版日期:
2019-11-05
- Title:
- Graph-regularized, sparse discriminant, non-negative matrix factorization
- Keywords:
- non-negative matrix factorization; feature extraction; dimensionality reduction; manifold learning; maximum margin criterion; discriminant information; sparse constraints; linear representation
- 分类号:
- TP391.4
- 摘要:
- 非负矩阵分解是一种流行的数据表示方法，利用图正则化约束能有效地揭示数据之间的局部流形结构。为了更好地提取图像特征，给出了一种基于图正则化的稀疏判别非负矩阵分解算法（graph regularization sparse discriminant non-negative matrix factorization，GSDNMF-L2，1）。利用同类样本之间的稀疏线性表示来构建对应的图及权矩阵；以L2，1范数进行稀疏性约束；以最大间距准则为优化目标函数，利用数据集的标签信息来保持数据样本之间的流形结构和特征的判别性，并给出了算法的迭代更新规则。在若干图像数据集上的实验表明，GSDNMF-L2，1在特征提取方面的分类精度优于各对比算法。
- Abstract:
- Non-negative matrix factorization is a popular data representation method. Using graph regularization constraints can effectively reveal the local manifold structure between data. In order to better extract image features, a graph-regularized, sparse-discriminant, non-negative matrix factorization algorithm is proposed in this paper. The sparse linear representation between similar samples was used to construct the corresponding graph and weight matrix. The objective function using the maximum margin criterion with L2,1 -norm constraint was optimized, using the tag information of the dataset to maintain the manifold structure of samples and discrimination of characteristics, and the iterative update rules of the algorithm are given. Experiments were carried out on the ORL, AR, and COIL20 datasets. Compared with other algorithms, GSDNMF-L2,1 showed higher classification accuracy in feature extraction.
- 参考文献/References:
-
[1] WOLD S, ESBENSEN K, GELADI P. Principal component analysis[J]. Chemometrics and intelligent laboratory systems, 1987, 2(1/2/3):37-52.
[2] BELHUMEUR P N, HESPANHA J P, KRIEGMAN D J. Eigenfaces vs. Fisherfaces:recognition using class specific linear projection[C]//Proceedings of the 4th European Conference on Computer Vision. Cambridge, UK, 1996:45-58.
[3] LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755):788-791.
[4] WANG Yuan, JIA Yunde, HU Changbo, et al. Fisher non-negative matrix factorization for learning local features[C]//Proceedings of the 6th Asian Conference on Computer Vision. Jeju, Korea, 2004.
[5] ZAFEIRIOU S, TEFAS A, BUCIU I, et al. Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification[J]. IEEE transactions on neural networks, 2006, 17(3):683-695.
[6] KOTSIA I, ZAFEIRIOU S, PITAS I. A novel discriminant non-negative matrix factorization algorithm with applications to facial image characterization problems[J]. IEEE transactions on information forensics and security, 2007, 2(3):588-595.
[7] GU Quanquan, ZHOU Jie. Two dimensional maximum margin criterion[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. Taipei, Taiwan, 2009:1621-1624.
[8] LI Haifeng, JIANG Tao, ZHANG Keshu. Efficient and robust feature extraction by maximum margin criterion[J]. IEEE transactions on neural networks, 2006, 17(1):157-165.
[9] LU Yuwu, LAI Zhihui, XU Yong, et al. Nonnegative discriminant matrix factorization[J]. IEEE transactions on circuits and systems for video technology, 2017, 27(7):1392-1405.
[10] CAI Deng, HE Xiaofei, HAN Jiawei, et al. Graph regularized nonnegative matrix factorization for data representation[J]. IEEE transactions on pattern analysis and machine intelligence, 2011, 33(8):1548-1560.
[11] LONG Xianzhong, LU Hongtao, PENG Yong, et al. Graph regularized discriminative non-negative matrix factorization for face recognition[J]. Multimedia tools and applications, 2014, 72(3):2679-2699.
[12] LIAO Qing, ZHANG Qian. Local coordinate based graph-regularized NMF for image representation[J]. Signal processing, 2016, 124:103-114.
[13] LI Xuelong, CUI Guosheng, DONG Yongsheng. Graph regularized non-negative low-rank matrix factorization for image clustering[J]. IEEE transactions on cybernetics, 2017, 47(11):3840-3853.
[14] SHANG Fanhua, JIAO L C, WANG Fei. Graph dual regularization non-negative matrix factorization for co-clustering[J]. Pattern recognition, 2012, 45(6):2237-2250.
[15] MENG Yang, SHANG Ronghua, JIAO Licheng, et al. Feature selection based dual-graph sparse non-negative matrix factorization for local discriminative clustering[J]. Neurocomputing, 2018, 290:87-99.
[16] EGGERT J, KORNER E. Sparse coding and NMF[C]//Proceedings of 2004 IEEE International Joint Conference on Neural Networks. Budapest, Hungary, 2004:2529-2533.
[17] BELKIN M, NIYOGI P, SINDHWANI V. Manifold regularization:a geometric framework for learning from labeled and unlabeled examples[J]. Journal of machine learning research, 2006, 7(1):2399-2434.
[18] HOU C, JING W, YI W, et al. Local linear transformation embedding[J]. Neurocomputing, 2009, 72(10-12):2368-2378.
[19] LI H, LIU D, WANG D. Manifold regularized reinforcement learning[J]. IEEE transactions on neural networks & learning systems, 2017, 29(4):932-943.
[20] HOYER P O. Non-negative matrix factorization with sparseness constraints[J]. Journal of machine learning research, 2004, 5:1457-1469.
[21] NIE Feiping, HUANG Heng, CAI Xiao, et al. Efficient and robust feature selection via joint l2, 1-norms minimization[C]//Proceedings of the 23rd International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada, 2010:1813-1821.
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备注/Memo
收稿日期:2018-11-26。
基金项目:2018年江苏省研究生科研创新计划项目（KYCX18_1871）.
作者简介:徐慧敏,女,1994年生,硕士研究生,主要研究方向为数字图像处理、模式识别;陈秀宏,男,1964年生,教授,博士后,主要研究方向为数字图像处理和模式识别、优化理论与方法等。发表学术论文120余篇。
通讯作者:徐慧敏.E-mail:1215625771@qq.com
更新日期/Last Update:
2019-12-25