Abstract
Subspace clustering is a technique which aims to find the underlying low-dimensional subspace in a high-dimensional data space. Since the multi-view data exists generally and it can effectively improve the performance of the learning task in real-world applications, multi-view subspace clustering has gained lots of attention in recent years. In this paper, to further improve the clustering performance of multi-view subspace clustering, we propose a novel subspace clustering method based on a global low-rank affinity matrix. In our method, we introduce a global affinity matrix, and use a sparse term to fit the difference between the global affinity matrix and local affinity matrices. Meanwhile, our method explores the global consistent information from different views and simultaneously guarantees the global affinity matrix for segmentation is low-rank. The objective function can be solved efficiently by the inexact augmented Lagrange multipliers (ALM) optimization method. Experiments results on two public real face datasets demonstrate that our method can improve the clustering performance against with the state-of-the-art methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhou, C., Zhang, C., Fu, H., Wang, R., Cao, X.: Multi-cue augmented face clustering. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, MM 2015, Brisbane, Australia, 26–30 October 2015, pp. 1095–1098 (2015)
Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20–25 June 2009, Miami, Florida, USA, pp. 2790–2797 (2009)
Cheng, B., Liu, G., Wang, J., Huang, Z., Yan, S.: Multi-task low-rank affinity pursuit for image segmentation. In: IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, 6–13 November 2011, pp. 2439–2446 (2011)
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning (2013). arXiv preprint. arXiv:1304.5634
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2013)
Vidal, R.: A tutorial on subspace clustering. IEEE Signal Process. Mag. 28(2), 52–68 (2010)
Cao, X., Zhang, C., Fu, H., Liu, S., Zhang, H.: Diversity-induced multi-view subspace clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12 June 2015, pp. 586–594 (2015)
Gao, H., Nie, F., Li, X., Huang, H.: Multi-view subspace clustering. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 7–13 December 2015, pp. 4238–4246 (2015)
Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices (2010). arXiv preprint. arXiv:1009.5055
Liu, G., Lin, Z., Yu, Y.: Robust subspace segmentation by low-rank representation. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), 21–24 June 2010, Haifa, Israel, pp. 663–670 (2010)
Elhamifar, E., Vidal, R.: Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2765–2781 (2013)
Lu, C., Min, H., Zhao, Z., Zhu, L., Huang, D., Yan, S.: Robust and efficient subspace segmentation via least squares regression. In: Computer Vision - ECCV 2012–12th European Conference on Computer Vision, Florence, Italy, 7–13 October 2012, Part VII, pp. 347–360 (2012)
Acknowledgements
The work was supported by NSFC (U1435214, 61305068), Jiangsu Nature Science Foundation (JSNSF) (BK20130581), and the Open Project Program of State Key Laboratory for Novel Software Technology (KFKT2016B16).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Qi, L., Shi, Y., Wang, H., Yang, W., Gao, Y. (2016). Multi-view Subspace Clustering via a Global Low-Rank Affinity Matrix. 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_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-46257-8_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46256-1
Online ISBN: 978-3-319-46257-8
eBook Packages: Computer ScienceComputer Science (R0)