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

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.