Big Data Research ›› 2022, Vol. 8 ›› Issue (3): 103-114.doi: 10.11959/j.issn.2096-0271.2022027

• STUDY • Previous Articles     Next Articles

A semi-supervised deep learning algorithm combining consistency regularization and manifold regularization

Jie WANG1,2, Songyan ZHANG1,2, Jiye LIANG1,2   

  1. 1 School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
    2 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan 030006, China
  • Online:2022-05-15 Published:2022-05-01
  • Supported by:
    The National Natural Science Foundation of China(61976184);The Key Project of Research and Development Plan of Shanxi Province(201903D121162);The 1331 Engineering Project of Shanxi Province

Abstract:

Semi-supervised learning has been widely used in big data analysis.Currently, one of the hot research topics in semisupervised deep learning is consistency-based methods.However, such methods do not take into account the manifold structure of the data, which may cause a portion of similar samples to get very different outputs, resulting in degraded classifier performance.To address this problem, a semi-supervised deep learning algorithm that combines consistency regularization with manifold regularization was proposed.The algorithm imposed a consistency constraint on the model while constructing a graph and adding a smoothing loss to achieve smoothing within the local neighborhood of each sample point and between adjacent (connected) sample points, thus improving the generalization performance of the semisupervised learning algorithm.The results on several image and text datasets show that the proposed algorithm is more effective compared with other semi-supervised deep learning algorithms.

Key words: semi-supervised deep learning, consistency regularization, manifold regularization, smoothness constraint

CLC Number: 

No Suggested Reading articles found!