Dimension reduction algorithm of battlefield situation data based on local preserving discriminant projection

Chuntian Hu; Ruini Wang

doi:10.1117/12.2682527

26 May 2023 Dimension reduction algorithm of battlefield situation data based on local preserving discriminant projection

Chuntian Hu, Ruini Wang

Author Affiliations +

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127002C (2023) https://doi.org/10.1117/12.2682527
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

Aiming at the problem that a large number of high-dimensional nonlinear data restrict the efficiency of combat decision making in battlefield situation, a new battlefield situation data dimensionality reduction algorithm based on local preserving discriminant projection was proposed. Based on the traditional manifold learning to ensure local information, the algorithm makes full use of the category information of battlefield situation data. In addition, intra-class divergence matrix and inter-class divergence matrix were established according to the maximum marginal criterion, so as to ensure that similar data were more clustered and dissimilar data more dispersed in the low-dimensional space after dimensionality reduction embedding, so as to improve the separability performance of situation data.

Citation Download Citation

Chuntian Hu and Ruini Wang "Dimension reduction algorithm of battlefield situation data based on local preserving discriminant projection", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127002C (26 May 2023); https://doi.org/10.1117/12.2682527

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