基于稀疏字典的李群机器学习算法

doi:10.16451/j.cnki.issn1003-6059.202005007

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Abstract Lie group machine learning(LML) theory is widely applied to data representation and processing in image set classification, and satisfactory results are obtained. Therefore, a method of Lie group dictionary learning based on sparse dictionary is proposed. Firstly, the covariance matrix is employed to model the image set, and the Lie group structure composed of covariance matrix is analyzed. Logarithmic map is applied to map the data into the linear space to obtain the distance matrix of the data. Then, landmark multi-dimensional scaling is employed to realize dimension reduction of data and reduce the computational cost. Finally, Fisher discriminant dictionary learning is applied for classification. The experiments on YTC dataset indicate the good performance of the proposed algorithm in robustness and accuracy.

Key words： Lie Group Machine Learning Sparse Representation Landmark Multiple Dimensional Scaling Lie Group Dictionary Learning Image Set Classification

Received: 24 September 2019

ZTFLH:

TP 391

Fund:Supported by National Key Research and Development Program of China(No.2018YFA07070,2018YFA0701701) , National Na-tural Science Foundation of China(No.61373093,61402310,61672364,61672365)

About author:: (XIONG Xiaodong, master student. His research interests include Lie group machine learning and feature extraction.);(LI Fanzhang(Corresponding author), master, professor. His research interests include dynamic fuzzy machine learning, lie group machine, neuromorphic synergy machine learning and multidimensional collaborative education.);(WANG Bangjun, Ph.D. candidate, lecturer. His research interests include machine learning and data mining.);(LIANG Helan, Ph.D. candidate, lectu-rer. Her research interests include enterprise modeling, workflow management and service computing.)

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	XIONG Xiaodong
	LI Fanzhang
	WANG Bangjun
	LIANG Helan

Cite this article:

XIONG Xiaodong,LI Fanzhang,WANG Bangjun等. Lie Group Machine Learning Based on Sparse Dictionary[J]. , 2020, 33(5): 449-457.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202005007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I5/449

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