Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (10): 2320-2329.DOI: 10.3778/j.issn.1673-9418.2101075

• Artificial Intelligence • Previous Articles     Next Articles

Fast Multi-view Privileged Random Vector Function Link Network

WU Tianyu+(), WANG Shitong   

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2021-01-21 Revised:2021-03-16 Online:2022-10-01 Published:2021-03-23
  • About author:WU Tianyu, born in 1995, M.S. candidate. His research interests include artificial intelligence and pattern recognition.
    WANG Shitong, born in 1964, professor, Ph.D. supervisor, member of CCF. His research interests include artificial intelligence, pattern recognition, etc.
  • Supported by:
    National Natural Science Foundation of China(61972181)


吴天宇+(), 王士同   

  1. 江南大学 人工智能与计算机学院,江苏 无锡 214122
  • 通讯作者: + E-mail:
  • 作者简介:吴天宇(1995—),男,甘肃酒泉人,硕士研究生,主要研究方向为人工智能、模式识别。
  • 基金资助:


In reality, feature data are usually obtained from different ways or levels for the same object, and the data obtained are called multi-view data. It is of research value to mine and utilize multi-view data and shows some advan-tages over traditional single-view learning. An important issue in multi-view learning (MVL) is how to meet the consistency between perspectives while maintaining complementarities between perspectives. In order to solve the above problem, based on the concepts including multi-view leaning and learning using privileged information (LUPI), a fast multi-view privileged RVFL (FMPRVFL) is proposed based on random vector function link network (RVFL) to accomplish multi-view classification tasks. The basic idea of FMPRVFL lies in the use of additional information from other views on average as the privileged information to supervise the classification in the current view. The objective function of FMPRVFL designed in such a mutually supervised manner can be optimized with analytical solutions, thus accelerating the training of FMPRVFL. It is revealed in the theoretical analysis that in contrast to classical multi-view learning, FMPRVFL can provide extra generalization capability. The results of an experiment on 64 datasets demonstrate that FMPRVFL outperforms other comparative methods both in average testing accuracy and running time.

Key words: multi-view learning (MVL), privileged information, random vector function link network (RVFL)



关键词: 多视角学习(MVL), 特权信息, 随机向量函数连接网络(RVFL)

CLC Number: