Abstract
Two-dimensional partial least squares (2DPLS) is an effective two-view data analysis technique. However, conventional 2DPLS only takes into account the column information of two-dimensional images. In this paper, we simultaneously consider the column-wise and row-wise information of two-dimensional face images. We first propose a row-based two-dimensional PLS (r2DPLS) approach and then further present a novel double-directional PLS (D\(^{2}\)PLS) method. The proposed D\(^{2}\)PLS method can be optimized by two eigenvalue subproblems. Experimental results on the AR, Yale, and AT&T face databases show that our D\(^{2}\)PLS method can overall achieve better recognition accuracy than existing related methods.
Supported by Undergraduate Education and Teaching Reform Project of Yangzhou University under Grant YZUJX2016-32C; National Natural Science Foundation of China under Grants 61402203, 61703362, and 61611540347; Natural Science Foundation of Jiangsu Province of China under Grants BK20161338 and BK20170513; Yangzhou Science Project Fund of China under Grants YZ2016238 and YZ2017292; Excellent Young Backbone Teacher (Qing Lan) Project and Scientific Innovation Project Fund of Yangzhou University of China under Grant 2017CXJ033.
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References
Wold, H.: Soft modelling by latent variables: the non-linear iterative partial least squares (NIPALS) approach. Perspect. Probab. Stat. 12(S1), 117–142 (1975)
Hotelling, H.: Relations between two sets of variates. Biometrika 28, 321–377 (1936)
Aleix, M.M., Avinash, C.K.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)
Hubert, M., Branden, K.V.: Robust methods for partial least squares regression. J. Chemometr. 17(10), 537–549 (2003)
Kondylis, A., Hadi, A.S.: Derived components regression using the bacon algorithm. Comput. Stat. Data Anal. 51(2), 556–569 (2006)
Chen, H., Sun, Y., Gao, J., Hu, Y., Yin, B.: Solving partial least squares regression via manifold optimization approaches. IEEE Trans. Neural Netw. Learn. Syst. 30(2), 588–600 (2019)
Xie, Q., Tang, L., Li, W., John, V., Hu, Y.: Principal model analysis based on partial least squares (2019). arXiv: 1902.02422
Liu, H., Ma, Z., Han, J., Chen, Z., Zheng, Z.: Regularized partial least squares for multi-label learning. Int. J. Mach. Learn. Cybern. 9(2), 335–346 (2018)
Talukdar, U., Hazarika, S.M., Gan, J.Q.: A kernel partial least square based feature selection method. Pattern Recogn. 83, 91–106 (2018)
Tao, J.-L., Zhang, J.-M., Wang, L.-J., Shen, X.-J., Zha, Z.-J.: Near-duplicate video retrieval through toeplitz kernel partial least squares. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, W.-H., Vrochidis, S. (eds.) MMM 2019. LNCS, vol. 11296, pp. 352–364. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05716-9_29
Qiao, J., Wang, G., Li, W., Li, X.: A deep belief network with PLSR for nonlinear system modeling. Neural Netw. 104, 68–79 (2018)
Liquet, B., de Micheaux, P.L., Hejblum, B.P., Thiébautt, R.: Group and sparse group partial least square approaches applied in genomics context. Bioinformatics 32(1), 35–42 (2016)
Sun, Q.S.: Research on feature extraction and image recognition based on correlation projection analysis. Ph.D. dissertation. Nanjing University of Science and Technology, Nanjing (2006)
Yang, M.-L., Sun, Q.-S., Xia, D.-S.: Two-dimensional partial least squares and its application in image recognition. In: Huang, D.-S., Wunsch, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. CCIS, vol. 15, pp. 208–215. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85930-7_28
Zhang, Y.X., Huang, S., Feng, X., Zhang, J.H., Bu, W.B., Yang, D.: Two dimensional non-negative sparse partial least squares for face recognition. In: ICMEW, pp. 1–6, IEEE, Chengdu (2014)
Chu, M.T., Watterson, J.L.: On a multivariate eigenvalue problem: I. Algebraic theory and a power method. SIAM J. Sci. Comput. 14(5), 1089–1106 (1993)
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Yuan, YH. et al. (2019). D\(^{2}\)PLS: A Novel Bilinear Method for Facial Feature Fusion. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_44
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DOI: https://doi.org/10.1007/978-3-030-36808-1_44
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