Abstract
Lately, there has been a shift to multi-task learning. Multi-task learning performs better than the classical single task learning by learning from the training signals inherent in all the tasks. Inspired by multi-task least squares twin support vector machine, we propose a robust multi-task least squares twin support vector machine. In the proposed work, we introduce an error factor which successfully handles the noise. The proposed model is easy to implement and fast. This allows the model to be of direct application to larger and real-world data sets. In addition, the model deals with nonlinear data patterns by using kernel trick.
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
Similar content being viewed by others
References
Abdulnabi, A.H., Wang, G., Lu, J., Jia, K.: Multi-task CNN model for attribute prediction. IEEE Trans. Multimed. 17(11), 1949–1959 (2015)
Argyriou, A., Evgeniou, T., Pontil, M.: Convex multi-task feature learning. Mach. Learn. 73(3), 243–272 (2008)
Azad-Manjiri, M., Amiri, A., Sedghpour, A.S.: ML-SLSTSVM: a new structural least square twin support vector machine for multi-label learning. Pattern Anal. Appl. 23(1), 295–308 (2020)
Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997)
Evgeniou, T., Pontil, M.: Regularized multi-task learning, pp. 109–117 (2004). https://doi.org/10.1145/1014052.1014067
Fung, G.M., Mangasarian, O.L.: Multicategory proximal support vector machine classifiers. Mach. Learn. 59(1–2), 77–97 (2005)
Gong, P., Ye, J., Zhang, C.: Robust multi-task feature learning. In: KDD: Proceedings. International Conference on Knowledge Discovery & Data Mining, 2012, pp. 895–903 (2012)
Han, L., Zhang, Y.: Learning tree structure in multi-task learning. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, pp. 397–406. ACM, New York, NY, USA (2015). https://doi.org/10.1145/2783258.2783393
He, X., Mourot, G., Maquin, D., Ragot, J., Beauseroy, P., Smolarz, A., Grall-Maës, E.: Multi-task learning with one-class SVM. Neurocomputing 133, 416–426 (2014)
Jayadeva, Khemchandani, R., Chandra, S.: Twin support vector machines for pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 29(5), 905–910 (2007). https://doi.org/10.1109/TPAMI.2007.1068
Kumar, M.A., Gopal, M.: Least squares twin support vector machines for pattern classification. Expert Syst. Appl. 36(4), 7535–7543 (2009)
Li, Y., Tian, X., Liu, T., Tao, D.: On better exploring and exploiting task relationships in multitask learning: joint model and feature learning. IEEE Trans. Neural Netw. Learn. Syst. 29(5), 1975–1985 (2018)
Li, Y., Tian, X., Song, M., Tao, D.: Multi-task proximal support vector machine. Pattern Recognit. 48(10), 3249–3257 (2015)
Lu, L., Lin, Q., Pei, H., Zhong, P.: The ALS-SVM based multi-task learning classifiers. Appl. Intell. 48(8), 2393–2407 (2018)
Mei, B., Xu, Y.: Multi-task least squares twin support vector machine for classification. Neurocomputing (2019)
Qi, K., Liu, W., Yang, C., Guan, Q., Wu, H.: Multi-task joint sparse and low-rank representation for the scene classification of high-resolution remote sensing image. Remote Sens. 9(1) (2017). https://doi.org/10.3390/rs9010010
Tanveer, M., Rajani, T., Rastogi, R., Shao, Y.: Comprehensive review on twin support vector machines. arXiv preprint arXiv:2105.00336 (2021)
Widmer, C., Kloft, M., Görnitz, N., Rätsch, G.: Efficient training of graph-regularized multitask SVMs. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 633–647. Springer (2012)
Xie, F., Pang, X., Xu, Y.: Pinball loss-based multi-task twin support vector machine and its safe acceleration method. Neural Comput. Appl. 1–17 (2021)
Xie, X., Sun, S.: Multitask twin support vector machines, pp. 341–348 (2012). https://doi.org/10.1007/978-3-642-34481-7_42
Xie, X., Sun, S.: Multitask centroid twin support vector machines. Neurocomputing 149, 1085–1091 (2015)
Xu, Z., Kersting, K.: Multi-task learning with task relations. In: 2011 IEEE 11th International Conference on Data Mining, pp. 884–893. IEEE (2011)
Xue, Y., Beauseroy, P.: Multi-task learning for one-class SVM with additional new features. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 1571–1576. IEEE (2016)
Yang, H., King, I., Lyu, M.R.: Multi-task learning for one-class classification. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2010)
Zhang, Y., Yang, Q.: A survey on multi-task learning. arXiv preprint arXiv:1707.08114 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rastogi, R., Hussain, M. (2022). Robust Multi-task Least Squares Twin Support Vector Machines for Classification. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_29
Download citation
DOI: https://doi.org/10.1007/978-981-19-0840-8_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0839-2
Online ISBN: 978-981-19-0840-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)