A method for feature extraction based on SVD and machine learning

2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation

Mao, Wei^1 ; Huang, Shuxian^2 ; Liu, Xin^1 ; Liu, Hongyan^1 ; Liu, Jiaqi^3 ; Shu, Yi^1

National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, 100076, China^1

Beijing Institute of Precision Mechatronics and Controls, 100076, China^2

Beijing Institute of Long March Vehicle, 100076, China^3

关键词: Feature decomposition;    Feature extraction methods;    High-accuracy;    Interpretability;    Spatial objects;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/569/5/052010/pdf
DOI  :  10.1088/1757-899X/569/5/052010

来源: IOP

【 摘 要 】

By studying the shortcomings of feature, which extracted from Radar-Cross Section(RCS),using mathematical and statistical method, using the idea of extracting abstract features in image recognition and speech recognition by artificial intelligence for reference[2][3]. This paper explores the possibility of extracting abstract features of target's RCS sequence, and proposes an abstract feature extraction method of RCS sequence based on singular value decomposition(SVD) feature decomposition. Because of the poor interpretability of abstract features, four different machine learning algorithms are used to classify the extracted Abstract features. The experimental results show that the machine learning algorithm can classify different types of spatial objects with high accuracy, which shows that the RCS features of different spatial objects can be characterized by abstract features.