Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1809-1818.DOI: 10.3778/j.issn.1673-9418.2103101

• Artificial Intelligence • Previous Articles     Next Articles

Chunk Incremental Canonical Correlation Analysis

PAN Yu1, CHEN Xiaohong+(), LI Shunming2, LI Jiyong3   

  1. 1. College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    2. College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
    3. Sichuan Aerospace Zhongtian Power Equipment Co., Ltd., Chengdu 610100, China
  • Received:2021-03-29 Revised:2021-06-15 Online:2022-08-01 Published:2021-06-09
  • About author:PAN Yu, born in 1997, M.S. candidate. Her res-earch interests include pattern recognition and artificial intelligence.
    CHEN Xiaohong, born in 1977, associate pro-fessor, M.S. supervisor. Her research interests include pattern recognition and artificial intellig-ence.
    LI Shunming, born in 1962, professor, Ph.D. supervisor. His research interests include intel-ligent machine learning and artificial neural net-work, intelligent fault diagnosis, etc.
    LI Jiyong, born in 1985, Ph.D., senior engineer. His research interests include vibration signal processing and vibration suppression.
  • Supported by:
    the National Natural Science Foundation of China(11971231);the National Natural Science Foundation of China(12111530001);the National Key Research and Development Program of China(2018YFB2003300)


潘玉1, 陈晓红+(), 李舜酩2, 李纪永3   

  1. 1. 南京航空航天大学 理学院,南京 211106
    2. 南京航空航天大学 能源与动力学院,南京 211106
    3. 四川航天中天动力装备有限责任公司,成都 610100
  • 通讯作者: +E-mail:
  • 作者简介:潘玉(1997—),女,安徽阜阳人,硕士研究生,主要研究方向为模式识别、人工智能。
  • 基金资助:


For the large-scale dynamic data stream, incremental learning is an effective and efficient technique and is widely used in machine learning. Incremental dimensionality reduction algorithms have been proposed by many scholars. As an improved canonical correlation analysis (CCA) method based on incremental learning, incremental canonical correlation analysis (ICCA) can effectively deal with the problem of dimensionality reduction of high-dimensional multi-view data stream. However, there is a drawback in this approach that the projection vector must be updated once for each new sample, which consumes a lot of time on the issue of online learning. Aiming at this problem, chunk incremental canonical correlation analysis (CICCA) is proposed in this paper. It can avoid the calculation of sample covariance matrices and process batch data stream directly. The main projection vector is updated each time with the newly added batch sample information, which is used to revise and update the projection vector of the previous step. Further, the other projection vectors are calculated in the orthogonal complement space of the projection vector. Therefore, data can be got from low-dimensional spaces. Experimental results show that the classification performance of CICCA is comparable to CCA and ICCA, but the training time is greatly reduced on synthetic dataset and real dataset.

Key words: canonical correlation analysis (CCA), dimensionality reduction, incremental learning, multi-view classification



关键词: 典型相关分析(CCA), 数据降维, 增量学习, 多视图分类

CLC Number: