Abstract
Multi-view clustering, which explores complementary information between multiple distinct feature sets, has received considerable attention. For accurate clustering, all data with the same label should be clustered together regardless of their multiple views. However, this is not guaranteed in existing approaches. To address this issue, we propose Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization (AMVNMF), which uses label information as hard constraints to ensure data with same label are clustered together, so that the discriminating power of new representations are enhanced. Besides, AMVNMF provides a viable solution to learn the weight of each view adaptively with only a single parameter. Using \(L_{2,1}\)-norm, AMVNMF is also robust to noises and outliers. We further develop an efficient iterative algorithm for solving the optimization problem. Experiments carried out on five well-known datasets have demonstrated the effectiveness of AMVNMF in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information.
Keywords
- Nonnegative matrix factorization
- Multi-view learning
- Semi-supervised learning
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)
Blanz, V., Tarr, M.J., Bülthoff, H.H., Vetter, T.: What object attributes determine canonical views? Percept.-Lond. 28(5), 575–600 (1999)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100. ACM (1998)
Cai, X., Nie, F., Huang, H.: Multi-view k-means clustering on big data. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2598–2604. AAAI Press (2013)
Cao, X., Zhang, C., Fu, H., Liu, S., Zhang, H.: Diversity-induced multi-view subspace clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–594 (2015)
Hidru, D., Goldenberg, A.: Equinmf: graph regularized multiview nonnegative matrix factorization. arXiv preprint arXiv:1409.4018 (2014)
Kumar, A., Rai, P., Daume, H.: Co-regularized multi-view spectral clustering. In: Advances in Neural Information Processing Systems, pp. 1413–1421 (2011)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Liu, C.H., Chaudhuri, A.: Reassessing the 3/4 view effect in face recognition. Cognition 83(1), 31–48 (2002)
Liu, H., Wu, Z., Li, X., Cai, D., Huang, T.S.: Constrained nonnegative matrix factorization for image representation. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1299–1311 (2012)
Liu, H., Yang, G., Wu, Z., Cai, D.: Constrained concept factorization for image representation. IEEE Trans. Cybern. 44(7), 1214–1224 (2014)
Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of SDM, vol. 13, pp. 252–260. SIAM (2013)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
Tzortzis, G., Likas, A.: Kernel-based weighted multi-view clustering. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 675–684. IEEE (2012)
Wang, H., Nie, F., Huang, H.: Multi-view clustering and feature learning via structured sparsity. In: Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 352–360 (2013)
Zhang, X., Zhao, L., Zong, L., Liu, X., Yu, H.: Multi-view clustering via multi-manifold regularized nonnegative matrix factorization. In: 2014 IEEE International Conference on Data Mining (ICDM), pp. 1103–1108. IEEE (2014)
Zhu, X., Ghahramani, Z., Lafferty, J., et al.: Semi-supervised learning using Gaussian fields and harmonic functions. In: ICML, vol. 3, pp. 912–919 (2003)
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
Wang, J., Wang, X., Tian, F., Liu, C.H., Yu, H., Liu, Y. (2016). Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-46672-9_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46671-2
Online ISBN: 978-3-319-46672-9
eBook Packages: Computer ScienceComputer Science (R0)