Abstract
Multi-view learning is a hot research topic in different research fields. Recently, a model termed multi-view intact space learning has been proposed and drawn a large amount of attention. The model aims to find the latent intact representation of data by integrating information from different views. However, the model has two obvious shortcomings. One is that the model needs to tune two regularization parameters. The other is that the optimization algorithm is too time-consuming. Based on the unit intact space assumption, we propose an improved model, termed multi-view unit intact space learning, without introducing any prior parameters. Besides, an efficient algorithm based on proximal gradient scheme is designed to solve the model. Extensive experiments have been conducted on four real-world datasets to show the effectiveness of our method.
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\(L_0\) is the initial Lipschitz constant in backtracking step-size rule. More detailed description about the theorem for proof and step-size setting can be found in [2].
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Acknowledgments
This work was supported by Key Research and Development Program of Guangdong (2015B010108001), NSFC (61502543), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014) and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (No. 2016TQ03X542).
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Lin, KY., Wang, CD., Meng, YQ., Zhao, ZL. (2017). Multi-view Unit Intact Space Learning. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_18
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DOI: https://doi.org/10.1007/978-3-319-63558-3_18
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