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.
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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).
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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
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