Abstract
With the wide applications of cross-view data, cross-view Classification tasks draw much attention in recent years. Nevertheless, an intrinsic imperfection existed in cross-view data is that the data of the different views from the same semantic space are further than that within the same view but from different semantic spaces. To solve this special phenomenon, we design a novel discriminative subspace learning model via low-rank representation. The model maps cross-view data into a low-dimensional subspace. The main contributions of the proposed model include three points. 1) A self-representation model based on dual low-rank models is adopted, which can capture the class and view structures, respectively. 2) Two local graphs are designed to enforce the view-specific discriminative constraint for instances in a pair-wise way. 3) The global constraint on the mean vector of different classes is developed for further cross-view alignment. Experimental results on classification tasks with several public datasets prove that our proposed method outperforms other feature learning methods.
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Acknowledgement
This work was supported in part by National Natural Science Foundation of China under Grant 61501147, University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province under Grant UNPYSCT-2018203, Natural Science Foundation of Heilongjiang Province under Grant YQ2019F011, Fundamental Research Foundation for University of Heilongjiang Province under Grant LGYC2018JQ013, and Postdoctoral Foundation of Heilongjiang Province under Grant LBH-Q19112.
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Li, A., Ding, Y., Chen, D., Sun, G., Jiang, H. (2020). Discriminative Subspace Learning for Cross-view Classification with Simultaneous Local and Global Alignment. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2020. Communications in Computer and Information Science, vol 1265. Springer, Singapore. https://doi.org/10.1007/978-981-15-7670-6_15
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DOI: https://doi.org/10.1007/978-981-15-7670-6_15
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