Incremental Discriminant Analysis in Tensor Space.
To study incremental machine learning in tensor space, this paper proposes incremental tensor discriminant analysis. The algorithm employs tensor representation to carry on discriminant analysis and combine incremental learning to alleviate the computational cost. This paper proves that the algorithm can be unified into the graph framework theoretically and analyzes the time and space complexity in detail. The experiments on facial image detection have shown that the algorithm not only achieves sound performance compared with other algorithms, but also reduces the computational issues apparently.
College of Information Science and Technology, Chengdu University, Chengdu 610106, China ; Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan, Chengdu 610106, China.
Liu
L
Chang
College of Information Science and Technology, Chengdu University, Chengdu 610106, China ; Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan, Chengdu 610106, China.
Zhao
Z
Weidong
College of Information Science and Technology, Chengdu University, Chengdu 610106, China ; Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan, Chengdu 610106, China.
Yan
Y
Tao
College of Information Science and Technology, Chengdu University, Chengdu 610106, China ; Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan, Chengdu 610106, China.
Pu
P
Qiang
College of Information Science and Technology, Chengdu University, Chengdu 610106, China ; Key Laboratory of Pattern Recognition and Intelligent Information Processing in Sichuan, Chengdu 610106, China.
Du
D
Xiaodan
D000465:Algorithms; D016002:Discriminant Analysis; D005145:Face; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007858:Learning; D010363:Pattern Recognition, Automated / Q000379:methods