Abstract
Locality Preserving Projection (LPP) is a dimensional reduction method that has been widely used in various fields. While traditional LPP only uses a single projection matrix to reduce the dimension and preserve the locality structure of data, it may cause the single matrix may not handle these two tasks well at the same time. Therefore, in this paper, we proposed relaxed sparse locality presenting projection (RSLPP) which introduces two different projection matrices to better accomplish the two tasks. The addition of another projection matrix can help the original projection matrix has more freedom to select the appropriate feature for preserving the local structure of data. The experimental results on two data sets prove the effectiveness of the method.
This work was supported in part by the National Natural Science Foundation of China under Grant 61772141, Grant 62006048 and Grant 61972102, in part by Science and Technology Planning Project of Guangdong Province, China, under Grant 2019B020208001, Grant 2019B110210002, and in part by the Guangzhou Science and Technology Planning Project under Grant 201903010107 and Grant 201802010042 .
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Jiang, L., Fang, X., Han, N. (2021). Multiple Projections Learning for Dimensional Reduction. In: Zhang, Y., Xu, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2020. Lecture Notes in Computer Science(), vol 12606. Springer, Cham. https://doi.org/10.1007/978-3-030-69244-5_14
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DOI: https://doi.org/10.1007/978-3-030-69244-5_14
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