Abstract
CCA is a powerful tool for analyzing paired multi-view data. However, when facing semi-paired multi-view data which widely exist in real-world problems, CCA usually performs poorly due to its requirement of data pairing between different views in nature. To cope with this problem, we propose a semi-paired variant of CCA named SemiPCCA based on the probabilistic model for CCA. Experiments with artificially generated samples demonstrate the effectiveness of the proposed method.
Chapter PDF
Similar content being viewed by others
Keywords
References
Chen, X., Chen, S., Xue, H., Zhou, X.: A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data. Pattern Recognition 45(5), 2005–2018 (2012)
Lampert, C.H., Krömer, O.: Weakly-paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 566–579. Springer, Heidelberg (2010)
Gu, J., Chen, S., Sun, T.: Localization with Incompletely Paired Data in Complex Wireless Sensor Network. IEEE Transactions on Wireless Communications 10(9), 2841–2849 (2011)
Blaschko, M., Lampert, C., Gretton, A.: Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 133–145. Springer, Heidelberg (2008)
Kimura, A., Kameoka, H., Sugiyama, M., Nakano, T.: SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations. In: 20th International Conference on Pattern Recognition (ICPR), pp. 2933–2936. IEEE Press, Istanbul (2010)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. The Journal of Machine Learning 7, 2399–2434 (2006)
Bach, F.R., Jordan, M.I.: A Probability Interpretation of Canonical Correlation Analysis. Technical Report 688, Department of Statistics, Universityof California, Berkeley (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Zhang, B., Hao, J., Ma, G., Yue, J., Shi, Z. (2014). Semi-paired Probabilistic Canonical Correlation Analysis. In: Shi, Z., Wu, Z., Leake, D., Sattler, U. (eds) Intelligent Information Processing VII. IIP 2014. IFIP Advances in Information and Communication Technology, vol 432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44980-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-662-44980-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44979-0
Online ISBN: 978-3-662-44980-6
eBook Packages: Computer ScienceComputer Science (R0)