电子学报 ›› 2022, Vol. 50 ›› Issue (3): 533-539.DOI: 10.12263/DZXB.20210105

• 学术论文 • 上一篇    下一篇

基于自编码器和超图的半监督宽度学习系统

王雪松1,2, 张翰林1,2, 程玉虎1,2()   

  1. 1.地下空间智能控制教育部工程研究中心, 江苏 徐州 221116
    2.中国矿业大学信息与控制工程学院, 江苏 徐州 221116
  • 收稿日期:2021-01-13 修回日期:2021-03-17 出版日期:2022-03-25 发布日期:2022-03-25
  • 通讯作者: 程玉虎
  • 作者简介:王雪松 女,1974年生,安徽泗县人.现为地下空间智能控制教育部工程研究中心主任,中国矿业大学信息与控制工程学院教授、博士生导师.研究领域为机器学习及模式识别.E-mail:wangxuesongcumt@163.com
    张翰林 男,1998年生,山东聊城人.现为中国矿业大学信息与控制工程学院硕士研究生.研究领域为图像处理与分析.E-mail:18252111368@163.com
    程玉虎(通讯作者) 男,1973年生,安徽淮南人.现为中国矿业大学信息与控制工程学院教授、博士生导师.研究领域为机器学习及模式识别.
  • 基金资助:
    国家自然科学基金(61976215)

Autoencoder and Hypergraph-Based Semi-Supervised Broad Learning System

WANG Xue-song1,2, ZHANG Han-lin1,2, CHENG Yu-hu1,2()   

  1. 1.Engineering Research Center of Intelligent Control for Underground Space,Ministry of Education,Xuzhou,Jiangsu 221116,China
    2.School of Information and Control Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221116,China
  • Received:2021-01-13 Revised:2021-03-17 Online:2022-03-25 Published:2022-03-25
  • Contact: CHENG Yu-hu

摘要:

常规宽度学习系统(Broad Learning System, BLS)通常采用的线性稀疏特征提取方法难以对数据的复杂非线性特征进行有效表征.此外,当标记样本量较少时,BLS的泛化性能难以得到保证.为此,提出一种基于自编码器和超图的半监督宽度学习系统(Autoencoder and Hypergraph-based Semi-supervised BLS, AH-SBLS).主要步骤为:首先,使用包括标记样本和无标记样本在内的全部样本训练自编码器,利用训练好的自编码器自动提取数据的复杂非线性特征;其次,将自编码器特征层中的特征作为AH-SBLS的特征节点并对其进行宽度拓展;然后,构造半监督超图以挖掘标记样本和无标记样本间的高阶流形关系,并将超图正则项引入宽度学习系统的目标函数中;最后,利用岭回归对目标函数进行求解,实现对无标记样本的类别预测.在图像分类实验上的结果表明,AH-SBLS能够实现半监督分类且获得较高的分类精度.

关键词: 半监督, 宽度学习系统, 自编码器, 超图, 图像分类

Abstract:

The linear sparse feature extraction method used in the classical broad learning system(BLS) is difficult to extract the complex nonlinear features of data effectively. In addition, when the number of labeled samples is small, the generalization ability of BLS cannot be guaranteed. To solve these problems, a novel autoencoder and hypergraph-based semi-supervised BLS(AH-SBLS) is proposed. The main steps of AH-SBLS are described as follows. Firstly, we use all labeled and unlabeled samples to train the autoencoder, and then the trained autoencoder is used to extract the features of input data automatically. Secondly, the extracted features are viewed as the feature nodes of AH-SBLS and are further broadened. In the third step, a semi-supervised hypergraph is constructed to express the high-order manifold relationship between labeled and unlabeled samples, and the hypergraph regularization term is introduced into the objective function of AH-SBLS. Finally, the objective function of AH-SBLS is solved by ridge regression and thus the labels of unlabeled samples can be predicted. Experimental results of image classification show that AH-SBLS can achieve higher classification accuracy in semi-supervised classification tasks.

Key words: semi-supervised, broad learning system, autoencoder, hypergraph, image classification

中图分类号: