A dimension reduction method of situation knowledge based on Sparse Autoencoder
Chuang Wang, Li Song, Wenfeng Wei, Shijie Li, Jiayi Liu · 2020
Under the background of great changes in military science and technology theory, in order to solve the problem of massive high-dimensional situation knowledge processing in the process of battlefield situation assessment.The current dimensionality reduction methods often ignore the influence of algorithm complexity and model representation ability on dimensionality reduction when solving the massive dimensionality reduction problem of high-dimensional situation knowledge. In order to balance this problem, this paper proposes a situation knowledge dimension reduction method based on Sparse Autoencoder, which has a good performance in achieving dimension reduction of high-dimensional situation information and obtaining its abstract feature representation.