极限学习机
降维
投影(关系代数)
计算机科学
投影寻踪
人工智能
还原(数学)
自组织映射
维数之咒
非线性系统
机器学习
模式识别(心理学)
数据挖掘
工艺工程
数学
算法
人工神经网络
工程类
量子力学
几何学
物理
作者
Н Б Сапунова,A. O. Bogatyreva,Elena V. Liyaskina,В. В. Ревин
标识
DOI:10.1016/j.jbiotec.2019.05.181
摘要
This paper presents a novel dimensionality reduction technique based on ELM and SOM: ELM-SOM+. This technique preserves the intrinsic quality of Self-Organizing Map (SOM): it is nonlinear and suitable for big data. It also brings continuity to the projection using two Extreme Learning Machine (ELM) models, the first one to perform the dimensionality reduction and the second one to perform the reconstruction. ELM-SOM+ is tested successfully on nine diverse datasets. Regarding reconstruction error, the new methodology shows considerable improvement over SOM and brings continuity.
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