Semi-Supervised Classification Based on Transformed Learning

Kang Zhao; Liu Liang; Han Meng

doi:10.7544/issn1000-1239.202110811

Journal of Computer Research and Development > 2023 > 60(1): 103-111. > DOI: 10.7544/issn1000-1239.202110811 CSTR: 32373.14.issn1000-1239.202110811

Kang Zhao, Liu Liang, Han Meng. Semi-Supervised Classification Based on Transformed Learning[J]. Journal of Computer Research and Development, 2023, 60(1): 103-111. DOI: 10.7544/issn1000-1239.202110811

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Semi-Supervised Classification Based on Transformed Learning

1.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731
2.
School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731

Funds: This work was supported by the National Natural Science Foundation of China (61806045).

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Received Date: August 12, 2021
Revised Date: April 23, 2022
Available Online: February 10, 2023

Graphical Abstract

Abstract

Abstract

In recent years graph-based semi-supervised classification is one of the research hot topics in machine learning and pattern recognition. In general, this algorithm discovers the hidden information by constructing a graph and classifies the labels for unlabeled samples based on the structural information of the graph. Therefore, the performance of semi-supervised classification heavily depends on the quality of the graph, especially the graph construction algorithm and the quality of data. In order to solve the above problems, we propose to perform a semi-supervised classification based on transformed learning (TLSSC) in this paper. Unlike most existing semi-supervised classification algorithms that learn the graph using raw features, our algorithm seeks a representation (transformed coefficients) and performs graph learning and label propagation based on the learned representation. In particular, a unified framework that integrates representation learning, graph construction, and label propagation is proposed, so that it is alternately updated and mutually improved and can avoid the sub-optimal solution caused by the low-quality graph. Specially, the raw features are mapped into transformed representation by transformed learning, then learn a high-quality graph by self-expression and achieve classification performance by label propagation. Extensive experiments on face and subject data sets show that our proposed algorithm outperforms other state-of-the-art algorithms in most cases.
- semi-supervised classification,
- transformed learning,
- graph construction,
- label propagation,
- representation learning

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References (31)

References

[1]	许震,沙朝锋,王晓玲,等. 基于KL距离的非平衡数据半监督学习算法[J]. 计算机研究与发展,2010,47(1):81−87 Xu Zhen, Sha Chaofeng, Wang Xiaoling, et al. A semi-supervised learning algorithm from imbalanced data based on KL divergence[J]. Journal of Computer Research and Development, 2010, 47(1): 81−87 (in Chinese)
[2]	李宇峰,黄圣君,周志华. 一种基于正则化的半监督多标记学习方法[J]. 计算机研究与发展,2012,49(6):1272−1278 Li Yufeng, Huang Shengjun, Zhou Zhihua. Regularized semi-supervised multi-label learning[J]. Journal of Computer Research and Development, 2012, 49(6): 1272−1278 (in Chinese)
[3]	周志华. 基于分歧的半监督学习[J]. 自动化学报,2013,39(11):1871−1878 doi: 10.3724/SP.J.1004.2013.01871 Zhou Zhihua. Disagreement-based semi-supervised learning[J]. Acta Automatica Sinica, 2013, 39(11): 1871−1878 (in Chinese) doi: 10.3724/SP.J.1004.2013.01871
[4]	张晨光,张燕,张夏欢. 最大规范化依赖性多标记半监督学习方法[J]. 自动化学报,2015,41(9):1577−1588 Zhang Chenguang, Zhang Yan, Zhang Xiahuan. Normalized dependence maximization multi-label semi-supervised learning method[J]. Acta Automatica Sinica, 2015, 41(9): 1577−1588 (in Chinese)
[5]	陈荣,曹永锋,孙洪. 基于主动学习和半监督学习的多类图像分类[J]. 自动化学报,2011,37(8):954−962 Chen Rong, Cao Yongfeng, Sun Hong. Multi-class image classification with active learning and semi-supervised learning[J]. Acta Automatica Sinica, 2011, 37(8): 954−962 (in Chinese)
[6]	张永,陈蓉蓉,张晶. 基于交叉熵的安全Tri-training算法[J]. 计算机研究与发展,2021,58(1):60−69 doi: 10.7544/issn1000-1239.2021.20190838 Zhang Yong, Chen Rongrong, Zhang Jing. Safe Tri-training algorithm based on cross entropy[J]. Journal of Computer Research and Development, 2021, 58(1): 60−69 (in Chinese) doi: 10.7544/issn1000-1239.2021.20190838
[7]	李明,杨艳屏,占惠融. 基于局部聚类与图方法的半监督学习算法[J]. 自动化学报,2010,36(12):1655−1660 Li Ming, Yang Yanping, Zhan Huirong. Semi-supervised learning based on graph and local quick shift[J]. Acta Automatica Sinica, 2010, 36(12): 1655−1660 (in Chinese)
[8]	张震,汪斌强,李向涛,等. 基于近邻传播学习的半监督流量分类方法[J]. 自动化学报,2013,39(7):1100−1109 Zhang Zhen, Wang Binqiang, Li Xiangtao, et al. Semi-supervised traffic identification based on affinity propagation[J]. Acta Automatica Sinica, 2013, 39(7): 1100−1109 (in Chinese)
[9]	Bo Xiaofan, Kang Zhao, Zhao Zhitong, et al. Latent multi-view semi-supervised classification[C] //Proc of the 11th Asian Conf on Machine Learning. PMLR, 2019 [2022-01-27]. http://proceedings.mlr.press/v101/bo19a.html
[10]	Kang Zhao, Pan Haiqi, Hoi S C H, et al. Robust graph learning from noisy data[J]. IEEE Transactions on Cybernetics, 2020, 50(5): 1833−1843 doi: 10.1109/TCYB.2018.2887094
[11]	Kang Zhao, Xu Zenglin, Lu Xiao, et al. Self-weighted multiple kernel learning for graph-based clustering and semi-supervised classification[C] //Proc of the 27th Int Joint Conf on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2018: 2312−2318
[12]	刘钰峰,李仁发. 异构信息网络上基于图正则化的半监督学习[J]. 计算机研究与发展,2015,52(3):606−613 doi: 10.7544/issn1000-1239.2015.20131147 Liu Yufeng, Li Renfa. Graph regularized semi-supervised learning on heterogeneous information networks[J]. Journal of Computer Research and Development, 2015, 52(3): 606−613 (in Chinese) doi: 10.7544/issn1000-1239.2015.20131147
[13]	Zhu Xiaojin, Ghahramani Z. Learning from labeled and unlabeled data with label propagation, CMU-CALD-02-107[R]. Pittsburgh, PA: Carnegie Mellon University, 2002
[14]	Jebara T, Wang Jun, Chang Shifu. Graph construction and b-matching for semi-supervised learning[C] //Proc of the 26th Annual Int Conf on Machine Learning. New York: ACM, 2009: 441−448
[15]	Cheng Hong, Liu Zicheng, Yang Jie. Sparsity induced similarity measure for label propagation[C] //Proc of the 12th Int Conf on Computer Vision. Los Alamitos, CA: IEEE Computer Society, 2009: 317−324
[16]	Li Sheng, Fu Yun. Learning balanced and unbalanced graphs via low-rank coding[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 27(5): 1274−1287
[17]	Wang Fei, Zhang Changshui. Label propagation through linear neighborhoods[J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 20(1): 55−67
[18]	Nie Feiping, Cai Guohao, Li Xuelong. Multi-view clustering and semi-supervised classification with adaptive neighbours[C] //Proc of the 31st AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2017: 2408−2414
[19]	Kang Zhao, Guo Zipeng, Huang Shudong, et al. Multiple partitions aligned clustering[C] //Proc of the 28th Int Joint Conf on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2019: 2701−2707
[20]	Maggu J, Majumdar A, Chouzenoux E. Transformed subspace clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 33(4): 1796−1801
[21]	Ravishankar S, Bresler Y. Learning sparsifying transforms[J]. IEEE Transactions on Signal Processing, 2012, 61(5): 1072−1086
[22]	Ravishankar S, Wen B, Bresler Y. Online sparsifying transform learning—part I: Algorithms[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(4): 625−636 doi: 10.1109/JSTSP.2015.2417131
[23]	Ravishankar S, Bresler Y. Online sparsifying transform learning—part II: Convergence analysis[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(4): 637−646 doi: 10.1109/JSTSP.2015.2407860
[24]	Zhu Xiaojin, Ghahramani Z, Lafferty J D. Semi-supervised learning using Gaussian fields and harmonic functions[C] //Proc of the 20th Int Conf on Machine Learning. Palo Alto, CA: AAAI Press, 2003: 912−919
[25]	Nie Feiping, Wang Hua, Huang Heng, et al. Unsupervised and semi-supervised learning via ℓ1-norm graph [C] //Proc of the 13th IEEE Int Conf on Computer Vision. Los Alamitos, CA: IEEE Computer Society, 2011: 2268−2273
[26]	古楠楠,樊明宇,王迪,等. 基于仿射子空间稀疏表示的半监督分类[J]. 中国科学:信息科学,2015,45(8):985−1000 doi: 10.1360/N112015-00106 Gu Nannan, Fan Mingyu, Wang Di, et al. Semi-supervised classification based on affine subspace sparse representation[J]. SCIENTIA SINICA Informationis, 2015, 45(8): 985−1000 (in Chinese) doi: 10.1360/N112015-00106
[27]	Lu Canyi, Min Hai, Zhao Zhongqiu, et al. Robust and efficient subspace segmentation via least squares regression [C] //Proc of the 12th European Conf on Computer Vision. Berlin: Springer, 2012: 347−360
[28]	Mohar B, Alavi Y, Chartrand G, et al. The Laplacian spectrum of graphs[J]. Graph Theory, Combinatorics, and Applications, 1991, 2(12): 871−898
[29]	Chung F R K. Spectral Graph Theory[M]. Providence, Rhode Island: American Mathematical Society, 1997
[30]	Zhou Dengyong, Bousquet O, Lal T N, et al. Learning with local and global consistency[C] //Proc of the 16th Int Conf on Neural Information Processing Systems. Cambridge, MA: MIT Press, 2003: 321−328
[31]	Li Chunguang, Lin Zhouchen, Zhang Honggang, et al. Learning semi-supervised representation towards a unified optimization framework for semi-supervised learning[C] //Proc of the 15th IEEE Int Conf on Computer Vision. Los Alamitos, CA: IEEE Computer Society, 2015: 2767−2775

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