Yang Zhiyong, Zhang Taohong, Lu Jingcheng, Su Yuan, Zhang Dezheng, Duan Yaowu
Department of Computer, School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, 100083 China.
Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083 China.
Cogn Neurodyn. 2017 Oct;11(5):453-465. doi: 10.1007/s11571-017-9444-2. Epub 2017 Jun 10.
This paper studies the joint effect of V-matrix, a recently proposed framework for statistical inferences, and extreme learning machine (ELM) on regression problems. First of all, a novel algorithm is proposed to efficiently evaluate the V-matrix. Secondly, a novel weighted ELM algorithm called V-ELM is proposed based on the explicit kernel mapping of ELM and the V-matrix method. Though V-matrix method could capture the geometrical structure of training data, it tends to assign a higher weight to instance with smaller input value. In order to avoid this bias, a novel method called VI-ELM is proposed by minimizing both the regression error and the V-matrix weighted error simultaneously. Finally, experiment results on 12 real world benchmark datasets show the effectiveness of our proposed methods.
本文研究了V矩阵（一种最近提出的用于统计推断的框架）与极限学习机（ELM）在回归问题上的联合效应。首先，提出了一种新颖的算法来高效评估V矩阵。其次，基于ELM的显式核映射和V矩阵方法，提出了一种名为V-ELM的新颖加权ELM算法。虽然V矩阵方法可以捕捉训练数据的几何结构，但它倾向于给输入值较小的实例赋予更高的权重。为了避免这种偏差，通过同时最小化回归误差和V矩阵加权误差，提出了一种名为VI-ELM的新颖方法。最后，在12个真实世界基准数据集上的实验结果表明了我们所提出方法的有效性。
