Abstract
With the wide applications of cross-view data, cross-view Classification tasks draw much attention in recent years. Nevertheless, an intrinsic imperfection existed in cross-view data is that the data of the different views from the same semantic space are further than that within the same view but from different semantic spaces. To solve this special phenomenon, we design a novel discriminative subspace learning model via low-rank representation. The model maps cross-view data into a low-dimensional subspace. The main contributions of the proposed model include three points. 1) A self-representation model based on dual low-rank models is adopted, which can capture the class and view structures, respectively. 2) Two local graphs are designed to enforce the view-specific discriminative constraint for instances in a pair-wise way. 3) The global constraint on the mean vector of different classes is developed for further cross-view alignment. Experimental results on classification tasks with several public datasets prove that our proposed method outperforms other feature learning methods.
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
Similar content being viewed by others
References
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Belhumeur, P.-N., Hespanha, J.-P., Kriegman, D.-J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Cands, E.-J., Li, X., Ma, Y., et al.: Robust principal component analysis? J. ACM 58(3), 1–37 (2011)
Liu, G., Yan, S.: Latent low-rank representation for subspace segmentation and feature extraction. In: IEEE International Conference on Computer Vision (ICCV), pp. 1615–1622. (2011)
Li, S., Fu, Y.: Learning robust and discriminative subspace with low-rank constraints. IEEE Trans. Neural Networks Learn. Syst. 27(11), 2160–2173 (2016)
Wong, W.-K., Lai, Z., Wen, J., et al.: Low-rank embedding for robust image feature extraction. IEEE Trans. Image Process. 26(6), 2905–2917 (2017)
Ren, Z., Sun, Q., Wu, B., et al.: Learning latent low-rank and sparse embedding for robust image feature extraction. IEEE Trans. Image Process. 29, 2094–2107 (2020)
Li, A., Chen, D., Wu, Z., et al.: Self-supervised sparse coding scheme for image classification based on low-rank representation. PLoS ONE 13(6), 1–15 (2018)
Li, A., Liu, X., Wang, Y., et al.: Subspace structural constraint-based discriminative feature learning via nonnegative low-rank representation. PLoS ONE 14(5), 1–19 (2019)
Zhang, Z., Li, F., Zhao, M., et al.: Joint low-rank and sparse principal feature coding for enhanced robust representation and visual classification. IEEE Trans. Image Process. 25(6), 2429–2443 (2016)
Zhang, Z., Ren, J., Li, S., et al.: Robust subspace discovery by block-diagonal adaptive locality-constrained representation. In: ACM International Conference on Multimedia, pp. 1569–1577 (2019)
Zhang, Z., Wang, L., Li, S., et al.: Adaptive structure-constrained robust latent low-rank coding for image recovery. In: IEEE International Conference on Data Mining (ICDM), pp. 846–855 (2019)
Zhang, Z., Zhang, Y., Liu, G., et al.: Joint label prediction based semi-supervised adaptive concept factorization for robust data Representation. IEEE Trans. Knowl. Data Eng. 32(5), 952–970 (2020)
Kan, M., Shan, S., Zhang, H., et al.: Multi-view discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 188–194 (2016)
Ding, Z., Fu, Y.: Dual low-rank decompositions for robust cross-view learning. IEEE Trans. Image Process. 28(1), 194–204 (2019)
Li, A., Wu, Z., Lu, H., et al.: Collaborative self-regression method with nonlinear feature based on multi-task learning for image classification. IEEE Access 6, 43513–43525 (2018)
Zhang, Y., Zhang, Z., Qin, J., et al.: Semi-supervised local multi-manifold isomap by linear embedding for feature extraction. Pattern Recogn. 76, 662–678 (2018)
Cai, J.-F., Cands, E.-J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956–1982 (2010)
Yang, J., Yin, W., Zhang, Y., et al.: A fast algorithm for edge-preserving variational multichannel image restoration. SIAM J. Imaging Sci. 2(2), 569–592 (2009)
He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems, pp. 153–160 (2004)
Acknowledgement
This work was supported in part by National Natural Science Foundation of China under Grant 61501147, University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province under Grant UNPYSCT-2018203, Natural Science Foundation of Heilongjiang Province under Grant YQ2019F011, Fundamental Research Foundation for University of Heilongjiang Province under Grant LGYC2018JQ013, and Postdoctoral Foundation of Heilongjiang Province under Grant LBH-Q19112.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, A., Ding, Y., Chen, D., Sun, G., Jiang, H. (2020). Discriminative Subspace Learning for Cross-view Classification with Simultaneous Local and Global Alignment. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2020. Communications in Computer and Information Science, vol 1265. Springer, Singapore. https://doi.org/10.1007/978-981-15-7670-6_15
Download citation
DOI: https://doi.org/10.1007/978-981-15-7670-6_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7669-0
Online ISBN: 978-981-15-7670-6
eBook Packages: Computer ScienceComputer Science (R0)