Abstract
Semi-supervised learning techniques have been attracting increasing interests in many machine learning fields for its effectiveness in using labeled and unlabeled samples. however, the ultimate performance tend to be inaccurate or misleading due to the presence of heavy noise and outliers. This problem raises the need to develop the methods that can exploit data structure and also be robust to the noisy points. In this paper, a novel semi-supervised classification method, named adaptive graph learning for semi-supervised self-paced classification (AGLSSC in short), is proposed by integrating self-paced learning (SSL) regime and adaptive graph learning (AGL) strategy into a joint framework and experimentally evaluated. Specifically, AGLSSC automatically select import samples by adding a parameter that can measure the importance of samples in each iteration optimization process. In addition, in order to learn the internal relationship of samples from corrupt data, the proposed method adaptively learns an optimal sample similarity matrix while maintaining the local structure of the samples. In this case, the proposed model has strong robustness to noise points. Extensive experiments conducted on diverse benchmarks demonstrate that AGLSSC achieves the most outstanding performance compared to some state-of-the-art semi-supervised classification methods.
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References
Iscen A, Tolias G, Avrithis Y, Chum O (2019) Label propagation for deep semi-supervised learning. IEEE conference on computer vision and pattern recognition, pp 5070–5079. https://doi.org/10.1109/CVPR.2019.00521
Xiao M, Guo Y (2013) Semi-supervised representation learning for cross-lingual text classification, proceedings of the 2013 conference on empirical methods in natural language processing, 1465–1475
Li W, Sun M (2006) Semi-supervised learning for image annotation based on conditional random fields. Image Video Retr 4071:463–472
Zhang C, Cheng J, Tian Q (2019) Unsupervised and semi-supervised image classification with weak semantic consistency. IEEE Trans Multimed 21(10):2482–2491
Fei W, Jing XY, Zhou J, Ji YM, Lan C, Huang Q, Wang R (2019) Semi-supervised multi-view individual and sharable feature learning for webpage classification. The World Wide Web Conference 3349–3355
Wang Z, Wang Z, Young LY, Sun F, Zhu S (2019) SolidBin improving metagenome binning with semi-supervised normalized cut. Bioinform 35(21):4229–4238
Guangbo R, Jie Z, Yi MA, Zheng R (2010) Generative model based semi-supervised learning method of remote sensing image classification. J Remote Sens 14(6):1090–1104
Nigam K, Mccallum AK, Thrun S (2000) Text classification from labeled and unlabeled documents using EM. Mach Learn 39:103–134
Kingma DP, Rezende DJ, Mohamed S, Welling M (2014) Semi-supervised learning with deep generative models. Adv Neural Inf Process Syst 4:3581–3589
Chu Z, Li P, Xuegang H (2019) Co-training based on semi-supervised ensemble classification approach for multi-label data stream. International conference on big knowledge 58–65
Lan D, Wang Y, Xie W (2019) a semi-supervised method for sar target discrimination based on co-training. International Geoscience and Remote Sensing Symposium 9482–9485
Siyuan Q, Wei S, Zhishuai Z, Bo W, Alan LY (2018) Deep co-training for semi-supervised image recognition, computer vision—ECCV 2018 - 15th European Conference, 11219, 142–159
Chuck R, Martial H, Henry S (2005) Semi-supervised self-training of object detection models, 7th IEEE workshop on applications of computer vision, 29–36
Wu D, Shang M, Luo X, Xu J, Yan H, Deng W, Wang G (2018) Self-training semi-supervised classification based on density peaks of data. Neurocomputing 275:180–191
Collobert R, Sinz FH, Weston J, Bottou L (2006) Large scale transductive SVMs. J Mach Learn Res 7:1687–1712
Nie F, Xiang S, Jia Y, Zhang C (2009) Semi-supervised orthogonal discriminant analysis via label propagation. Pattern Recognit 42(11):2615–2627
Meng J, Cheolkon J (2015) Semi-supervised Bi-dictionary learning using smooth representation-based label propagation, 2015 International conference on cyber-enabled distributed computing and knowledge discovery, 239–242
Junliang MA, Bing XAC, Cheng D (2020) Graph based semi-supervised classification with probabilistic nearest neighbors. Pattern Recognit Lett 133:94–101
Zhu X, Ghahramani Z, Lafferty JD (2003) Semi-supervised learning using gaussian fields and harmonic functions, machine learning. Proceedings of the twentieth international conference 912–919
Dengyong Z, Olivier B, Thomas NL, Jason W, Bernhard S (2003) Learning with local and global consistency. Adv Neural Inf Process Syst 16(16):321–328
Wang M, Fu W, Hao S, Tao D, Wu X (2016) Scalable semi-supervised learning by efficient anchor graph regularization. IEEE Trans Knowl Data Eng 28(7):1864–1877
Yan S, Wang H (2009) Semi-supervised learning by sparse representation. Proceedings of the SIAM international conference on data mining 792–801
Gideon S, Mann AM (2007). Simple, robust, scalable semi-supervised learning via expectation regularization, machine learning, Proceedings of the twenty-fourth international conference 227:593–600
Philip S, Angelica IAR, Nicolas P, David C, Anita F, Carola-Bibiane S (2019) Semi-supervised learning with graphs: covariance based superpixels for hyperspectral image classification, IEEE international geoscience and remote sensing symposium, 592–595
de Sousa CAR, Solange OR, Gustavo EAPAB (2013) Influence of graph construction on semi-supervised learning, machine learning and knowledge discovery in databases—European Conference , 8190, 160–175
Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7(1):2399–2434
Girosi F, Jones M, Poggio T (1995) Regularization theory and neural networks architectures. Neural Comp 7(2):219–269
Yoshua B, Jerome L, Ronan C, Jason W (2009) Curriculum learning, Proceedings of the 26th annual international conference on machine learning, 382, 41–48
Pawan Kumar M, Benjamin P, Daphne K (2020) Self-paced learning for latent variable models, advances in neural information processing systems 23: 24th Annual conference on neural information processing systems 2010, 1189–1197
Fan Y, He R, Liang J, Bao-Gang H (2017) Learning self-paced (2017) an implicit regularization perspective, Proceedings of the thirty-first aaai conference on. artificial intelligence 1877–1883
Chang X, Tao D, Chao X (2015) Multi-view self-paced learning for clustering, Proceedings of the twenty-fourth international joint conference on. artificial intelligence 3974–3980
Feiping N, Xiaoqian W, Heng H (2014) Clustering and projected clustering with adaptive neighbors, The 20th ACM SIGKDD international conference on knowledge discovery and data mining, 977–986
James Steven Supancic III and Deva Ramanan, Self-Paced Learning for Long-Term Tracking, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2379–2386, (2013)
Gabriel H, Maria LS (2011) On approximate kkt condition and its extension to continuous variational inequalities. J Optim Theory Appl 149(3):528–539
Nie F, Li J, Li X (2016) Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. Proceedings of the twenty-fifth international joint conference on artificial intelligence 1881–1887
Cai X, Nie F, Cai W, Huang H (2013) Heterogeneous image features integration via multi-modal semi-supervised learning model. IEEE international conference on computer vision 1737–1744
Nie F, Cai G, Li X (2017) Multi-view clustering and semi-supervised classification with adaptive neighbours. Proceedings of the thirty-first AAAI conference on artificial intelligence 2408–2414
Chen L, Zhong Z (2019) Progressive graph-based subspace transductive learning for semi-supervised classification. IET Image Process 13(14):2753–2762
Xiaofeng Z, Bin S, Feng S, Yanbo C, Rongyao H, Jiangzhang G, Wenhai Z, Man L, Liye W, Yaozong G, Fei S, Dinggang S (2020) Joint prediction and time estimation of COVID-19 developing severe symptoms using chest ct scan, CoRR, abs/2005.03405, https://arxiv.org/abs/2005.03405
Shen H, Zhu Y, Zheng W, Zhu X (2020) Half-quadratic minimization for unsupervised feature selection on incomplete data. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3009632
Hu R, Zhu X, Zhu Y, Gan J (2020) Robust SVM with adaptive graph learning. World Wide Web 23:1945–1968
Zhu X, Zhang S, Zhu Y, Zhu P, Gao Y (2020) Unsupervised spectral feature selection with dynamic hyper-graph learning. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2020.3017250
Shijie H, Yuan Z, Yanrong G (2020) A brief survey on semantic segmentation with deep learning. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.11.118
Guo Y, Wu Z, Shen D (2020) Learning longitudinal classification-regression model for infant hippocampus segmentation. Neurocomputing. https://doi.org/10.1016/j.neucom.2019.01.108
Shen HT, Zhu X, Zhang Z, Wang S, Chen Y, Xu X, Shao J (2021) Heterogeneous data fusion for predicting mild cognitive impairment conversion. Inf Fus 66:54–63
Zhu X, Gan J, Lu G, Li J, Zhang S (2020) Spectral clustering via half-quadratic optimization. World Wide Web 23:1969–1988
Zhu X, Suk H-I, Wang L, Lee S-W, Shen D (2017) A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med Image Anal 38:205–214
Acknowledgements
This study was supported by the National Natural Science Foundation of China (No.61866006), and “BAGUI Scholar” Program of Guangxi Zhuang Autonomous Region of China, and Guangxi Innovation-Driven Development of Special Funds Project (Gui Ke AA18118047), and the Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS18-07).
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Chen, L., Lu, J. Adaptive Graph Learning for Semi-supervised Self-paced Classification. Neural Process Lett 54, 2695–2716 (2022). https://doi.org/10.1007/s11063-021-10453-6
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DOI: https://doi.org/10.1007/s11063-021-10453-6