Abstract
Canonical correlation analysis is a typical multiview representation learning technique, which utilizes within-set and between-set covariance matrices to analyze the correlation between two multidimensional datasets. However, it is quite difficult for the covariance matrix to measure the nonlinear relationship between features because of its linear structure. In this paper, we propose a multiple covariation projection (MCP) method to learn latent two-view representation, which has the ability to model the complicated feature relationship. The proposed MCP first constructs multiple general covariance matrices for modeling diverse feature relations, and then integrates them together via a linear ensemble strategy. At last, an efficient two-stage algorithm is designed for solutions. In addition, we further present a multiview MCP for dealing with the case of multiple (more than two) views. Experimental results on benchmark datasets show the effectiveness of our proposed MCP method in multiview classification and clustering tasks.
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
Andrew, G., Arora, R., Bilmes, J.A., Livescu, K.: Deep canonical correlation analysis. In: International Conference on Machine Learning (ICML), pp. 1247–1255. JMLR.org (2013)
Bach, F.R., Jordan, M.I.: Kernel independent component analysis. J. Mach. Learn. Res. 3, 1–48 (2002)
Chen, L., Wang, K., Li, M., Wu, M., Pedrycz, W., Hirota, K.: K-means clustering-based kernel canonical correlation analysis for multimodal emotion recognition in human-robot interaction. IEEE Trans. Industr. Electron. 70(1), 1016–1024 (2023)
Chen, M., Huang, L., Wang, C.D., Huang, D.: Multi-view clustering in latent embedding space. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 3513–3520. AAAI Press (2020)
Gu, Y., Wang, C., You, D., Zhang, Y., Wang, S., Zhang, Y.: Representative multiple kernel learning for classification in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 50(7), 2852–2865 (2012)
Hotelling, H.: Relations between two sets of variates. Biometrika 28(3/4), 321–377 (1936)
Karami, M., Schuurmans, D.: Deep probabilistic canonical correlation analysis. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 8055–8063. AAAI Press (2021)
Kettenring, J.R.: Canonical analysis of several sets of variables. Biometrika 58(3), 433–451 (1971)
Kim, T., Kittler, J., Cipolla, R.: Discriminative learning and recognition of image set classes using canonical correlations. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1005–1018 (2007)
Sharma, A., Jacobs, D.W.: Bypassing synthesis: pls for face recognition with pose, low-resolution and sketch. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 593–600. IEEE (2011)
Sun, Q., Zeng, S., Liu, Y., Heng, P., Xia, D.: A new method of feature fusion and its application in image recognition. Pattern Recogn. 38(12), 2437–2448 (2005)
Uurtio, V., Bhadra, S., Rousu, J.: Large-scale sparse kernel canonical correlation analysis. In: International Conference on Machine Learning (ICML), pp. 6383–6391. PMLR (2019)
Wang, W., Arora, R., Livescu, K., Bilmes, J.A.: On deep multi-view representation learning. In: International Conference on Machine Learning (ICML), pp. 1083–1092. JMLR.org (2015)
Xu, M., Zhu, Z., Zhang, X., Zhao, Y., Li, X.: Canonical correlation analysis with \(L_{2,1}\)-norm for multiview data representation. IEEE Trans. Cybern. 50(11), 4772–4782 (2020)
Yuan, Y., Li, J., Li, Y., Gou, J., Qiang, J.: Learning unsupervised and supervised representations via general covariance. IEEE Signal Process. Lett. 28, 145–149 (2021)
Yuan, Y., et al.: OPLS-SR: a novel face super-resolution learning method using orthonormalized coherent features. Inf. Sci. 561, 52–69 (2021)
Yuan, Y., et al.: Learning canonical f-correlation projection for compact multiview representation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19238–19247. IEEE (2022)
Yuan, Y., Sun, Q., Zhou, Q., Xia, D.: A novel multiset integrated canonical correlation analysis framework and its application in feature fusion. Pattern Recogn. 44(5), 1031–1040 (2011)
Zhang, L., Wang, L., Bai, Z., Li, R.: A self-consistent-field iteration for orthogonal canonical correlation analysis. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 890–904 (2022)
Acknowledgement
This work is supported by the National Natural Science Foundation of China under grants 62176126 and 62076217, and the China Postdoctoral Science Foundation under grant 2020M670995.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yuan, YH., Qian, P., Li, J., Qiang, J., Zhu, Y., Li, Y. (2024). Many Is Better Than One: Multiple Covariation Learning for Latent Multiview Representation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1963. Springer, Singapore. https://doi.org/10.1007/978-981-99-8138-0_18
Download citation
DOI: https://doi.org/10.1007/978-981-99-8138-0_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8137-3
Online ISBN: 978-981-99-8138-0
eBook Packages: Computer ScienceComputer Science (R0)