Pseudoinverse Matrix Decomposition Based Incremental Extreme Learning Machine with Growth of Hidden Nodes
Pseudoinverse Matrix Decomposition Based Incremental Extreme Learning Machine with Growth of Hidden Nodes
复制标题
DOI：
10.5391/ijfis.2016.16.2.125
复制
发表时间：
2016-06-30
影响因子：
1.3
通讯作者：
Kim, Euntai
中科院分区：
文献类型：
--
作者：
Kassani, Peyman Hosseinzadeh;Kim, Euntai
本研究的建议是一个快速版本的传统的极端学习机（ELM），称为伪逆矩阵分解为基础的增量ELM（PDI-ELM）。ELM中的一个主要问题是确定隐藏节点的数量。在这项研究中，隐藏节点的数量自动确定。该模型是一个增量版本的ELM，它增加了神经元的ELM网络的误差最小化的目标。为了加快模型的速度，在当前迭代中考虑了前一步的伪逆信息。为了显示PDI-ELM的能力，它被应用到加州欧文大学（UCI）存储库中的一些基准分类数据集。与ELM学习器和其他两个版本的增量式ELM相比，PDI-ELM的速度更快。
The proposal of this study is a fast version of the conventional extreme learning machine (ELM), called pseudoinverse matrix decomposition based incremental ELM (PDI-ELM). One of the main problems in ELM is to determine the number of hidden nodes. In this study, the number of hidden nodes is automatically determined. The proposed model is an incremental version of ELM which adds neurons with the goal of minimization the error of the ELM network. To speed up the model the information of pseudoinverse from previous step is taken into account in the current iteration. To show the ability of the PDI-ELM, it is applied to few benchmark classification datasets in the University of California Irvine (UCI) repository. Compared to ELM learner and two other versions of incremental ELM, the proposed PDI-ELM is faster.