A robust clustering algorithm based on extreme learning machine

被引：0

作者：

Du, Meng ^{[2
]}

Zhang, Jing ^{[1
,2
]}

Wu, Bin ^{[2
]}

Feng, Lin ^{[1
,2
]}

机构：

[1] School of Computer Science and Technology, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian

[2] School of Innovation Experiment, Dalian University of Technology, Dalian

来源：

Journal of Information and Computational Science | 2015年 / 12卷 / 13期

关键词：

Auto Encoder; Extreme Learning Machine; Restricted Boltzmann Machine;

D O I：

10.12733/jics20106433

中图分类号：

学科分类号：

摘要：

Original ELM-AE algorithm combines Extreme Learning Machine (ELM) with Auto Encoder (AE) to map data to a nonlinear high-dimensional space. This procedure can extract better sample characteristics when solving unsupervised clustering problems. ELM-AE adopts a solution procedure which is similar to ELM to improve the solution speed. However, ELM-AE has some problems. The solution of the system is not stable because of the random input-layer weights. Meanwhile, if the number of hidden layers is small, model fitting effect will be significantly reduced. To solve the above problems, we propose a Robust ELM-AE (RELM-AE) clustering algorithm. This method trains Restricted Boltzmann Machine (RBM) to get stable input-layer weights, and then minimizes the output error to obtain the output-layer weights. Compared with the original model of ELM-AE, this method achieves more stable and accurate results, which makes it more suitable for solving unsupervised clustering problems. ©, 2015, Journal of Information and Computational Science. All right reserved.

引用

页码：4951 / 4958

页数：7

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