Similarity Preserving Unsupervised Feature Selection based on Sparse Learning

被引:3
|
作者
Zare, Hadi [1 ]
Parsa, Mohsen Ghasemi [1 ]
Ghatee, Mehdi [2 ]
Alizadeh, Sasan H. [3 ]
机构
[1] Univ Tehran, Fac New Sci & Technol, Tehran, Iran
[2] Amirkabir Univ Technol, Dept Comp Sci, Tehran, Iran
[3] IRAN Telecommun Res Ctr, Dept Informat Technol, Tehran, Iran
关键词
Unsupervised feature selection; Cluster analysis; Similarity preserving; Sparse learning; FRAMEWORK;
D O I
10.1109/IST50524.2020.9345884
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Various feature selection methods have been recently proposed on different applications to reduce the computational burden of machine learning algorithms as well as the complexity of learned models. Preserving sample similarities and selecting discriminative features are two major factors should be satisfied, especially by unsupervised feature selection methods. This paper aims to propose a novel unsupervised feature selection approach which employs an l(2,1)-norm regularization model to preserve global and local similarities by minimizing an objective function. Cluster analysis is also incorporated in this framework to take the inherent structure of the data into account. The experimental results show the strength of the proposed approach as compared with the earlier well-known methods on a variety of standard datasets.
引用
收藏
页码:50 / 55
页数:6
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