Abstract
Over the past decades, extensive studies on multi-view learning and Universum learning have been witnessed in pattern recognition and machine learning. Incorporating multi-view learning and Universum learning together, we propose a novel supervised dimensionality reduction method for multi-view data accompanied by Universum data, termed Universum discriminant canonical correlation analysis (UDCCA). UDCCA exploits inter-view information by means of the within-class correlation as well as the within-Universum correlation between different views, and at the same time, utilizes intra-view discriminant information captured from the target samples and Universum data of each view. In the low-dimensional discriminant space, the within-class correlation of the target samples is maximized, and the correlation of the Universum data is minimized, and simultaneously the scatter among target samples and Universum data is also maximized. Experimental results on real-world multi-view datasets show its effectiveness compared to other related state-of-art dimensionality reduction methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Glenisson, P., Mathys, J., Moor, B.D.: Meta-clustering of gene expression data and literature-based information. SIGKDD Explor. Newsl. 5(2), 101–112 (2003)
Chen, Q., Sun, S.: Hierarchical multi-view fisher discriminant analysis. In: Leung, C.S., Lee, M., Chan, Jonathan H. (eds.) ICONIP 2009. LNCS, vol. 5864, pp. 289–298. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10684-2_32
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. arXiv: 1304.5634 (2013)
Sun, S.: A survey of multi-view machine learning. Neural Comput. Appl. 23(7), 2031–2038 (2013)
Li, Y., Yang, M., Zhang, Z.: Multi-view representation learning: a survey from shallow methods to deep methods. arXiv Preprint, 14(8), 1–27 (2016)
Hotelling, H.: Relations between two sets of variates. Biometrika 28(3), 321–377 (1936)
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16(12), 2639–2664 (2004)
Sun, T., Chen, S., Yang, J., Shi, P.: A novel method of combined feature extraction for recognition. In: 8th IEEE International Conference on Data Mining, pp. 1043–1048 (2008)
Vapnik, V.N.: Estimation of Dependences Based on Empirical Data, vol. 4(356). Springer, New York (2006)
Weston, J., Collobert, R., Sinz, F., Bottou, L., Vapnik, V.: Inference with the Universum. In: 23rd International Conference of Machine Learning, pp. 1009–1016 (2006)
Sinz, F.H., Chapelle, O., Agarwal, A., Schölkopf, B.: An analysis of inference with the Universum. Adv. Neural. Inf. Process. Syst. 26, 1369–1376 (2008)
Dhar, S., Ramakrishnan, N., Cherkassky, V., Shah, M.: Universum learning for multiclass SVM. arXiv preprint arXiv:1609.09162 (2016)
Zhang, D., Wang, J., Wang, F., Zhang, C.: Semi-supervised classification with universum. In: SIAM International Conference on Data Mining, pp. 323–333 (2008)
Pan, S., Wu, J., Zhu, X., Long, G., Zhang, C.: Boosting for graph classification with universum. Knowl. Inf. Syst. 1–25, 53–77 (2016)
Wang, Z., Zhu, Y., Liu, W., Chen, Z., Gao, D.: Multi-view learning with Universum. Knowl. Based Syst. 70, 376–391 (2014)
Li, D., Zhu, Y., Wang, Z., Chong, C., Gao, D.: Regularized matrix-pattern-oriented classification machine with Universum. Neural Process. Lett. 45(3), 1077–1098 (2017)
Chen, X., Chen, S., Xue, H.: Universum linear discriminant analysis. Electron. Lett. 48(22), 1407–1409 (2012)
Chen, X., Ma, D.: Universum principal component analysis. In: International Conference on Information Technology and Management Engineering, pp. 236–241 (2014)
Qiu, J., Zhang, Y., Pan, Z., Yang, H., Ren, H., Li, X.: A novel semi-supervised approach for feature extraction. In: 2016 International Joint Conference on Neural Networks (2016)
Chen, S., Zhang, C.: Selecting informative Universum sample for semi-supervised learning. In: 21st International Joint Conference on Artificial Intelligence, vol. 1, pp. 1016–1021 (2009)
Sun, S., Xie, X., Yang, M.: Multiview uncorrelated discriminant analysis. IEEE Trans. Cybern. 46(12), 3272–3284 (2015)
Kan, M., Shan, S., Zhang, H., Lao, S., Chen, X.: Multi-View discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 188–194 (2016)
Fisher, R.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7(2), 179–188 (1936)
Maćkiewicz, A., Ratajczak, W.: Principal components analysis. Comput. Geosci. 19(3), 303–342 (1993)
Breukelen, M.V., Duin, R.P.W., Tax, D.M.J., Hartog, J.E.D.: Handwritten digit recognition by combined classifiers. Kybernetika 34(4), 381–386 (1998)
Kushmerick, N.: Learning to remove internet advertisements. In: 3rd Proceedings of the Annual Conference on Autonomous Agents, pp. 175–181 (1999)
Lampert, C.H., Nickischm, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: CVPR, pp. 951–958 (2009)
Acknowledgments
This work was supported by National Natural Science Foundations of China (NSFC) under Grant no. 61403193, 11471159, 61661136001 and NUAA Research Funding under Grant no. NP2014081 and NZ2015103.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chen, X., Yin, H., Hu, M., Wang, L. (2017). Universum Discriminant Canonical Correlation Analysis. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_61
Download citation
DOI: https://doi.org/10.1007/978-3-319-68935-7_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68934-0
Online ISBN: 978-3-319-68935-7
eBook Packages: Computer ScienceComputer Science (R0)