Abstract
Semi-supervised learning is concerned with such learning scenarios where only a small portion of training data are labeled. In multiview settings, unlabeled data can be used to regularize the prediction functions, and thus to reduce the search space. In this chapter, we introduce two categories of multiview semi-supervised learning methods. The first one contains the co-training style methods, where the prediction functions from different views are trained through their own objective, and each prediction function is improved by the others. The second one contains the co-regularization style methods, where a single objective function exists for the prediction functions from different views to be trained simultaneously.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th annual conference on computational learning theory, ACM, pp 92–100
Blum A, Mansour Y (2017) Efficient co-training of linear separators under weak dependence. In: Proceedings of the 30th annual conference on learning theory, pp 302–318
Chao G, Sun S (2019) Semi-supervised multi-view maximum entropy discrimination with expectation laplacian regularization. Inf Fusion 45:296–306
Khemchandani R, Chandra S et al (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910
Nigam K, Ghani R (2000) Analyzing the effectiveness and applicability of co-training. In: Proceedings of the 9th international conference on information and knowledge management, ACM, pp 86–93
Qi Z, Tian Y, Shi Y (2012) Laplacian twin support vector machine for semi-supervised classification. Neural Netw 35(11):46–53
Sindhwani V, Niyogi P, Belkin M (2005) A co-regularization approach to semi-supervised learning with multiple views. Proc ICML Work Learn Mult Views ACM 2005:74–79
Sun S (2011) Multi-view laplacian support vector machines. In: Proceedings of the 7th international conference on advanced data mining and applications, Springer, pp 209–222
Sun S, Jin F (2011) Robust co-training. Int J Pattern Recognit Artif Intell 25(7):1113–1126
Sun S, Shawe-Taylor J (2010) Sparse semi-supervised learning using conjugate functions. J Mach Learn Res 11(9):2423–2455
Sun S, Zhang Q (2011) Multiple-view multiple-learner semi-supervised learning. Neural Process Lett 34(3):229–240
Xie X, Sun S (2014) Multi-view laplacian twin support vector machines. Appl Intell 41(4):1059–1068
Yu S, Krishnapuram B, Rosales R, Rao RB (2011) Bayesian co-training. J Mach Learn Res 12(9):2649–2680
Zhou ZH, Zhan DC, Yang Q (2007) Semi-supervised learning with very few labeled training examples. In: Proceedings of the 22nd AAAI national conference on artificial intelligence, AAAI, vol 1, pp 675–680
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sun, S., Mao, L., Dong, Z., Wu, L. (2019). Multiview Semi-supervised Learning. In: Multiview Machine Learning. Springer, Singapore. https://doi.org/10.1007/978-981-13-3029-2_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-3029-2_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3028-5
Online ISBN: 978-981-13-3029-2
eBook Packages: Computer ScienceComputer Science (R0)