Abstract
Multi-view learning attempts to generate a classifier with a better performance by exploiting relationship among multiple views. Existing approaches often focus on learning the consistency and/or complementarity among different views. However, not all consistent or complementary information is useful for learning, instead, only class-specific discriminative information is essential. In this paper, we propose a new robust multi-view learning algorithm, called DICS, by exploring the Discriminative and non-discriminative Information existing in Common and view-Specific parts among different views via joint non-negative matrix factorization. The basic idea is to learn a latent common subspace and view-specific subspaces, and more importantly, discriminative and non-discriminative information from all subspaces are further extracted to support a better classification. Empirical extensive experiments on seven real-world data sets have demonstrated the effectiveness of DICS, and show its superiority over many state-of-the-art algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: COLT. pp. 92–100 (1998)
Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. TPAMI 33(8), 1548–1560 (2011)
Chaudhuri, K., Kakade, S.M., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: ICML, pp. 129–136 (2009)
Chu, M., Diele, F., Plemmons, R., Ragni, S.: Optimality, computation, and interpretation of nonnegative matrix factorizations. SIMAX (2004). http://users.wfu.edu/plemmons/papers/chu_ple.pdf
De Sa, V.R.: Spectral clustering with two views. In: ICML Workshop on Learning with Multiple Views, pp. 20–27 (2005)
Farquhar, J.D., Hardoon, D.R., Meng, H., Shawe-Taylor, J., Szedmak, S.: Two view learning: SVM-2K, theory and practice. In: NIPS, pp. 355–362 (2005)
Gönen, M., Alpaydın, E.: Multiple kernel learning algorithms. JMLR 12(July), 2211–2268 (2011)
Guan, Z., Zhang, L., Peng, J., Fan, J.: Multi-view concept learning for data representation. TKDE 27(11), 3016–3028 (2015)
Gupta, S.K., Phung, D., Adams, B., Tran, T., Venkatesh, S.: Nonnegative shared subspace learning and its application to social media retrieval. In: KDD, pp. 1169–1178 (2010)
Gupta, S.K., Phung, D., Adams, B., Venkatesh, S.: Regularized nonnegative shared subspace learning. DMKD 26(1), 57–97 (2013)
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16(12), 2639–2664 (2004)
Kan, M., Shan, S., Zhang, H., Lao, S., Chen, X.: Multi-view discriminant analysis. TPAMI 38(1), 188–194 (2016)
Kim, H., Choo, J., Kim, J., Reddy, C.K., Park, H.: Simultaneous discovery of common and discriminative topics via joint nonnegative matrix factorization. In: KDD, pp. 567–576 (2015)
Kim, J., He, Y., Park, H.: Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework. JGO 58(2), 285–319 (2014)
Kumar, A., Daumé, H.: A co-training approach for multi-view spectral clustering. In: ICML, pp. 393–400 (2011)
Kumar, A., Rai, P., Daume, H.: Co-regularized multi-view spectral clustering. In: NIPS, pp. 1413–1421 (2011)
Lee, H., Yoo, J., Choi, S.: Semi-supervised nonnegative matrix factorization. IEEE Sig. Process. Lett. 17(1), 4–7 (2010)
Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: SDM, pp. 252–260 (2013)
Liu, J., Jiang, Y., Li, Z., Zhou, Z.H., Lu, H.: Partially shared latent factor learning with multiview data. TNNLS 26(6), 1233–1246 (2015)
Nie, F., Li, J., Li, X.: Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In: IJCAI (2016)
Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Mach. Learn. 39(2), 103–134 (2000)
Shao, J., Meng, C., Tahmasian, M., Brandl, F., Yang, Q., Luo, G., Luo, C., Yao, D., Gao, L., Riedl, V., et al.: Common and distinct changes of default mode and salience network in schizophrenia and major depression. Brain Imaging Behav. 1–12 (2018). https://doi.org/10.1007/s11682-018-9838-8
Shao, J., Myers, N., Yang, Q., Feng, J., Plant, C., Böhm, C., Förstl, H., Kurz, A., Zimmer, C., Meng, C., et al.: Prediction of Alzheimer’s disease using individual structural connectivity networks. Neurobiol. Aging 33(12), 2756–2765 (2012)
Shao, J., Yang, Q., Wohlschlaeger, A., Sorg, C.: Discovering aberrant patterns of human connectome in Alzheimer’s disease via subgraph mining. In: ICDMW, pp. 86–93 (2012)
Shao, J., Yu, Z., Li, P., Han, W., Sorg, C., Yang, Q.: Exploring common and distinct structural connectivity patterns between schizophrenia and major depression via cluster-driven nonnegative matrix factorization. In: ICDM (2017)
Sharma, A., Kumar, A., Daume, H., Jacobs, D.W.: Generalized multi-view analysis: a discriminative latent space. In: CVPR, pp. 2160–2167 (2012)
Wang, H., Yang, Y., Li, T.: Multi-view clustering via concept factorization with local manifold regularization. In: ICDM, pp. 1245–1250 (2016)
Wang, W., Zhou, Z.H.: A new analysis of co-training. In: ICML, pp. 1135–1142 (2010)
Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 40(6), 1438–1446 (2010)
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. arXiv preprint arXiv:1304.5634 (2013)
Ye, H.J., Zhan, D.C., Miao, Y., Jiang, Y., Zhou, Z.H.: Rank consistency based multi-view learning: a privacy-preserving approach. In: CIKM, pp. 991–1000 (2015)
Zhang, M.L., Zhou, Z.H.: CoTrade: confident co-training with data editing. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41(6), 1612–1626 (2011)
Zhou, D., Burges, C.J.: Spectral clustering and transductive learning with multiple views. In: ICML, pp. 1159–1166 (2007)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (61403062, 41601025, 61433014,), Science-Technology Foundation for Young Scientist of SiChuan Province (2016JQ0007), State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2017490211), National key research and development program (2016YFB0502300).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Zhang, Z., Qin, Z., Li, P., Yang, Q., Shao, J. (2018). Multi-view Discriminative Learning via Joint Non-negative Matrix Factorization. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-91458-9_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91457-2
Online ISBN: 978-3-319-91458-9
eBook Packages: Computer ScienceComputer Science (R0)