Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (12): 2775-2787.DOI: 10.3778/j.issn.1673-9418.2103085

• Artificial Intelligence • Previous Articles     Next Articles

Incomplete Modality Transfer Learning via Latent Low-Rank Constraint

XU Guangsheng1,2,+(), WANG Shitong1,2   

  1. 1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
    2. Key Laboratory of Media Design and Software Technology of Jiangsu Province, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2021-03-24 Revised:2021-06-15 Online:2022-12-01 Published:2021-06-23
  • About author:XU Guangsheng, born in 1996, M.S. candidate.His research interests include artificial intelligence and machine learning.
    WANG Shitong, born in 1964, professor, Ph.D. supervisor, member of CCF. His research interests include artificial intelligence, pattern recognition, etc.
  • Supported by:
    Natural Science Foundation of Jiangsu Province(BK20191331)


徐光生1,2,+(), 王士同1,2   

  1. 1.江南大学 人工智能与计算机学院,江苏 无锡 214122
    2.江南大学 江苏省媒体设计与软件技术重点实验室,江苏 无锡 214122
  • 通讯作者: +E-mail:
  • 作者简介:徐光生(1996—),男,安徽芜湖人,硕士研究生,主要研究方向为人工智能、机器学习。
    王士同(1964—),男,江苏扬州人,教授,博士生导师,CCF 会员,主要研究方向为人工智能、模式识别等。
  • 基金资助:


When insufficient or incomplete multi-modality data are available in training, the corresponding classi-fication learning may lead to poor training performance or even failure. In order to tackle with this problem, the transfer learning algorithm called IMTL (incomplete modality transfer learning via latent low-rank constraint) is proposed in this paper. The proposed algorithm addresses the incomplete modality problem in two ways. Firstly, latent factors are introduced into a low-rank constrained subspace framework so as to mine missing modality infor-mation on the target domain. With the help of an auxiliary yet complete modality dataset, the proposed cross-modality and cross-dataset transfer learning strategy is used to help align data between modalities or datasets. Sec-ondly, a small amount of labeled target data is used to align the supervision information so as to maintain the internal structure of the target data during the transfer learning. Experimental results show that the proposed algorithm outperforms the previous transfer learning algorithms, and significantly improves the classification performance on the adopted incomplete target datasets.

Key words: transfer learning, incomplete modality, latent low-rank constraint



关键词: 迁移学习, 不完整模态, 潜在的低秩约束

CLC Number: