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Coupled locality discriminant analysis with globality preserving for dimensionality reduction

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Abstract

Dimensionality reduction plays a key role in pattern recognition. It can preserve essential and inherent feature information while reducing noise and redundant information contained in the high-dimensional raw data, which achieve performance improvement in subsequent tasks (e.g., classification and clustering). Locality preserving projection (LPP), as a typical method for dimensionality reduction, can explore the local sub-manifold of the raw data with the aid of K-nearest neighbor (KNN). However, LPP has some serious limitations: (1) the neighbor parameter is artificially set, and this leads to the problem that the size of the neighbor parameter may affect the performance of LPP in the application; (2) LPP, as a single-view method, cannot function in multi-view data; (3) LPP ignores both the discriminative information and the global linear relationship of the raw data. In response to these limitations, we propose a novel multi-view dimensionality reduction method called coupled locality discriminant analysis with globality preserving (CLDA-GP). CLDA-GP can learn a couple of optimal mappings so that different multi-view raw spaces can be mapped into a low-dimensional uniform elastic subspace while keeping the local sub-manifold and global linear relationship. It is also worth mentioning that CLDA-GP gives another strategy called local similarity self-learning (LSSL) to excavate the local manifold information of the multi-view data. By utilizing the LSSL strategy, CLDA-GP casts off the limitation of the neighbor parameter. Besides, CLDA-GP further introduces the supervision information of the raw data, which enables its discriminant power. The experiment results on the artificial and benchmark (COIL-20, GT, and Umist) datasets prove CLDA-GP outperforms the comparative methods, which also illustrate the effectiveness and feasibility of CLDA-GP.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61806006), the China Postdoctoral Science Foundation (Grant No. 2019 M660149), the Institute of Energy, Hefei Comprehensive National Science Center (Grant No. 19KZS203), Science and Technology Research Project of Wuhu City (Grant No. 2020yf48).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, 232001, Anhui, China

    Shuzhi Su, Gang Zhu, Bin Ge & Xingzhu Liang

  2. Institute of Energy, Hefei Comprehensive National Science Center, Hefei, 230031, Anhui, China

    Shuzhi Su

  3. School of Mechanical Engineering, Anhui University of Science & Technology, Huainan, 232001, Anhui, China

    Yanmin Zhu

  4. Institute of Environment-friendly Materials and Occupational Health of Anhui University of Science and Technology (Wuhu), Wuhu, 241003, China

    Xingzhu Liang

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  1. Shuzhi Su
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  2. Gang Zhu
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  3. Yanmin Zhu
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  4. Bin Ge
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  5. Xingzhu Liang
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Correspondence to Yanmin Zhu.

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Su, S., Zhu, G., Zhu, Y. et al. Coupled locality discriminant analysis with globality preserving for dimensionality reduction. Appl Intell 53, 7118–7131 (2023). https://doi.org/10.1007/s10489-022-03409-3

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