Abstract
Many real-world datasets are represented by multiple features or modalities which often provide compatible and complementary information to each other. In order to obtain a good data representation that synthesizes multiple features, researchers have proposed different multi-view subspace learning algorithms. Although label information has been exploited for guiding multi-view subspace learning, previous approaches either fail to directly capture the semantic relations between labeled items or unrealistically make Gaussian assumption about data distribution. In this paper, we propose a new multi-view nonnegative subspace learning algorithm called Multi-view Semantic Learning (MvSL). MvSL tries to capture the semantic structure of multi-view data by a novel graph embedding framework. The key idea is to let neighboring intra-class items be near each other while keep nearest inter-class items away from each other in the learned common subspace across multiple views. This nonparametric scheme can better model non-Gaussian data. To assess nearest neighbors in the multi-view context, we develop a multiple kernel learning method for obtaining an optimal kernel combination from multiple features. In addition, we encourage each latent dimension to be associated with a subset of views via sparseness constraints. In this way, MvSL is able to capture flexible conceptual patterns hidden in multi-view features. Experiments on two real-world datasets demonstrate the effectiveness of the proposed algorithm.
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Amini, M., Usunier, N., Goutte, C.: Learning from multiple partially observed views-an application to multilingual text categorization. In: Advances in Neural Information Processing Systems, pp. 28–36 (2009)
Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(8), 1548–1560 (2011)
Chen, N., Zhu, J., Xing, E.P.: Predictive subspace learning for multi-view data: a large margin approach. In: Advances in Neural Information Processing Systems, pp. 361–369 (2010)
Han, Y., Wu, F., Tao, D., Shao, J., Zhuang, Y., Jiang, J.: Sparse unsupervised dimensionality reduction for multiple view data. IEEE Transactions on Circuits and Systems for Video Technology 22(10), 1485–1496 (2012)
He, J., Chang, S.-F., Xie, L.: Fast kernel learning for spatial pyramid matching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–7. IEEE (2008)
Hidru, D., Goldenberg, A.: Equinmf: Graph regularized multiview nonnegative matrix factorization (2014). arXiv preprint arXiv:1409.4018
Hotelling, H.: Relations between two sets of variates. Biometrika 321–377 (1936)
Hoyer, P.O.: Non-negative sparse coding. In: Proceedings of the 2002 12th IEEE Workshop on Neural Networks for Signal Processing, pp. 557–565 (2002)
Jia, Y., Salzmann, M., Darrell, T.: Factorized latent spaces with structured sparsity. In: Advances in Neural Information Processing Systems, pp. 982–990 (2010)
Jiang, Y., Liu, J., Li, Z., Lu, H.: Semi-supervised unified latent factor learning with multi-view data. Machine Vision and Applications 25(7), 1635–1645 (2014)
Kalayeh, M., Idrees, H., Shah, M.: NMF-KNN: Image annotation using weighted multi-view non-negative matrix factorization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 184–191 (2014)
Kim, J., Monteiro, R., Park, H.: Group sparsity in nonnegative matrix factorization. In: SDM, pp. 851–862. SIAM (2012)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Li, H., Wang, M., Hua, X.-S.: Msra-mm 2.0: A large-scale web multimedia dataset. In: IEEE International Conference on Data Mining Workshops, pp. 164–169. IEEE (2009)
Lin, C.-J.: Projected gradient methods for nonnegative matrix factorization. Neural computation 19(10), 2756–2779 (2007)
Liu, H., Wu, Z., Li, X., Cai, D., Huang, T.S.: Constrained nonnegative matrix factorization for image representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7), 1299–1311 (2012)
Liu, J., Jiang, Y., Li, Z., Zhou, Z.-H., Lu, H.: Partially shared latent factor learning with multiview data (2014)
Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: Proc. of SDM, vol. 13, pp. 252–260 (2013)
Sha, F., Lin, Y., Saul, L.K., Lee, D.D.: Multiplicative updates for nonnegative quadratic programming. Neural Computation 19(8), 2004–2031 (2007)
Shawe-Taylor, N., Kandola, A.: On kernel target alignment. Advances in neural information processing systems 14, 367 (2002)
Wang, Y., Jia, Y.: Fisher non-negative matrix factorization for learning local features. In: Proc. Asian Conf. on Comp. Vision. Citeseer (2004)
Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40(6), 1438–1446 (2010)
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. arXiv preprint arXiv:1304.5634 (2013)
Yan, S., Xu, D., Zhang, B., Zhang, H.-J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(1), 40–51 (2007)
Zafeiriou, S., Tefas, A., Buciu, I., Pitas, I.: Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification. IEEE Transactions on Neural Networks 17(3), 683–695 (2006)
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Luo, P., Peng, J., Guan, Z., Fan, J. (2015). Multi-view Semantic Learning for Data Representation. In: Appice, A., Rodrigues, P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9284. Springer, Cham. https://doi.org/10.1007/978-3-319-23528-8_23
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