Design of Extreme Learning Machine with Smoothedl0Regularization

被引：3
|
作者：
Yang, Cuili [1 ]
Nie, Kaizhe [1 ]
Qiao, Junfei [1 ]
Li, Bing [2 ]
机构：
[1] Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
[2] Wuhan Univ Technol, Sch Econ, Wuhan 430070, Peoples R China
来源：
MOBILE NETWORKS & APPLICATIONS | 2020年 / 25卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Extreme learning machine; Sparsity; l(0)regularization; Network compactness; REGULARIZATION; LARS; ELM;
D O I：
10.1007/s11036-020-01587-3
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
In extreme learning machine (ELM), a large number of hidden nodes are required due to the randomly generated hidden layer. To improve network compactness, the ELM with smoothedl(0)regularizer (ELM-SL0 for short) is studied in this paper. Firstly, thel(0)regularization penalty term is introduced into the conventional error function, such that the unimportant output weights are gradually forced to zeros. Secondly, the batch gradient method and the smoothedl(0)regularizer are combined for training and pruning ELM. Furthermore, both the weak convergence and strong convergence of ELM-SL0 are investigated. Compared with other existing ELMs, the proposed algorithm obtains better performance in terms of estimation accuracy and network sparsity.
引用
收藏
页码：2434 / 2446
页数：13
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