Abstract
Different with typical supervised canonical correlation methods where intraclass and interclass information of samples are exploited at the same time for classification tasks, in this paper, we follow the principle of Occam’s razor and thus propose a new supervised multiview dimensionality reduction method for image recognition, called semi-discriminative multiview canonical correlations (SemiDMCCs), which takes partial class information into account but generates discriminative low-dimensional projections. Experimental results on benchmark databases show the more effectiveness of the proposed method, in contrast to existing feature reduction methods.
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References
Bickel, S., Scheffer, T.: Multi-view clustering. In: Proceedings of the IEEE International Conference on Data Mining, pp. 19–26 (2004)
Zhou, D., Burges, C.J.: Spectral clustering and transductive learning with multiple views. In: Proceedings of the 24th International Conference on Machine Learning, pp. 1159–1166 (2007)
Li, S.-Y., Jiang, Y., Zhou, Z.-H.: Partial multi-view clustering. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 1968–1974 (2014)
Zien, A., Ong, C.S.: Multiclass multiple kernel learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 1191–1198 (2007)
Qian, Q., Chen, S., Zhou, X.: Multi-view classification with cross-view must-link and cannot-link side information. Knowl.-Based Syst. 54, 137–146 (2013)
Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding. IEEE Trans. Syst. Man Cybern. B Cybern. 40, 1438–1446 (2010)
Han, Y., Wu, F., Tao, D., Shao, J., Zhuang, Y., Jiang, J.: Sparse unsupervised dimensionality reduction for multiple view data. IEEE Trans. CSVT 22, 1485–1496 (2012)
Hotelling, H.: Relations between two sets of variates. Biometrika 28, 321–377 (1936)
Kettenring, J.R.: Canonical analysis of several sets of variables. Biometrika 58, 433–451 (1971)
Rupnik, J., Shawe-Taylor, J.: Multi-view canonical correlation analysis. In: SiKDD (2010). http://ailab.ijs.si/dunja/SiKDD2010/Papers/Rupnik_Final.pdf
Su, Y., Fu, Y., Gao, X., Tian, Q.: Discriminant learning through multiple principal angles for visual recognition. IEEE Trans. Image Process. 21, 1381–1390 (2012)
Yuan, Y.-H., Sun, Q.-S.: Graph regularized multiset canonical correlations with applications to joint feature extraction. Pattern Recogn. 47, 3907–3919 (2014)
Shen, X., Sun, Q., Yuan, Y.: A unified multiset canonical correlation analysis framework based on graph embedding for multiple feature extraction. Neurocomputing 148, 397–408 (2015)
Kim, T.-K., Kittler, J., Cipolla, R.: Discriminative learning and recognition of image set class using canonical correlations. IEEE Trans. PAMI 29, 1005–1018 (2007)
Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K.: Occam’s razor. Inf. Process. Lett. 24, 377–380 (1987)
Chu, M.T., Watterson, J.L.: On a multivariate eigenvalue problem: I. algebraic theory and power method. SIAM J. Sci. Comput. 14, 1089–1106 (1993)
Wei, Q.-L., Wang, R.-S., Xu, B., Wang, J.-Y., Bai, W.-L.: Mathematical Programming and Optimization Design. National Defense Industry Press, Beijing (1984)
Sugiyama, M.: Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J. Mach. Learn. Res. 8, 1027–1061 (2007)
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. PAMI 23, 228–233 (2001)
Fu, Y., Yan, S., Huang, T.S.: Correlation metric for generalized feature extraction. IEEE Trans. PAMI 30, 2229–2235 (2008)
Leibe, B., Schiele, B.: Analyzing appearance and contour based methods for object categorization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 409–415 (2003)
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant Nos. 61402203, 61273251, 61170120, Natural Science Foundation of Jiangsu Province of China under Grant No. BK20161338, and the Fundamental Research Funds for the Central Universities under Grant No. JUSRP11458. Moreover, it is also supported by the Program for New Century Excellent Talents in University under Grant No. NCET-12-0881.
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Yuan, YH., Li, Y., Ji, HK., Ren, CG., Shen, XB., Sun, QS. (2016). Semi-discriminative Multiview Canonical Correlation Analysis for Recognition. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_28
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DOI: https://doi.org/10.1007/978-3-319-46257-8_28
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