Abstract
With the diversification of data sources, the multi-view data with multiple expressions have been appeared in various application scenarios. These multi-view data generally have high dimensions, large amounts and often lack of label information. Therefore, it is very important to learn multi-view data in an unsupervised way so as to analyze and excavate the potential valuable information. In this paper, we propose a multi-view locality preserving embedding algorithm with view similarity constraint for data dimension reduction. This algorithm not only preserves the local structure into low-dimensional space for each view, but also implements the similarity constraints between different views. On this basis, the algorithm looks for a joint embedding of low-dimensional subspace, so that the neighborhood among samples in original high-dimensional space can be maintained in the subspace, and the structures corresponding to different views are consistent with each other. This algorithm achieves good experimental results both in artificial data sets and multi-view data sets, which prove the correctness and feasibility of the algorithm.
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Acknowledgments
This work was supported by Natural Science Foundation of Jiangsu Province under grant nos. BK20161560, BK20171479, BK20161020 and National Science Foundation of China under grant nos. 61003116, 61432008, 61603193.
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He, Y., Cai, W., Yang, M., Song, F. (2019). Multi-view Locality Preserving Embedding with View Consistent Constraint for Dimension Reduction. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_27
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DOI: https://doi.org/10.1007/978-3-030-29551-6_27
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