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Multi-view dimensionality reduction via canonical random correlation analysis

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Abstract

Canonical correlation analysis (CCA) is one of the most well-known methods to extract features from multi-view data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take discriminant information into account. In this paper, we add discriminant information into CCA by using random cross-view correlations between within-class samples and propose a new method for multi-view dimensionality reduction called canonical random correlation analysis (RCA). In RCA, two approaches for randomly generating cross-view correlation samples are developed on the basis of bootstrap technique. Furthermore, kernel RCA (KRCA) is proposed to extract nonlinear correlations between different views. Experiments on several multi-view data sets show the effectiveness of the proposed methods.

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Authors and Affiliations

  1. College of Command Information Systems, PLA University of Science and Technology, Nanjing, 210007, China

    Yanyan Zhang, Jianchun Zhang & Zhisong Pan

  2. Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Daoqiang Zhang

Authors
  1. Yanyan Zhang
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  2. Jianchun Zhang
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  3. Zhisong Pan
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  4. Daoqiang Zhang
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Corresponding authors

Correspondence to Zhisong Pan or Daoqiang Zhang.

Additional information

Yanyan Zhang received the MS degree in computer science and application from Nanjing University of Aeronautics and Astronautics, China in 2010. Now she is a teaching assistant in PLA University of Science and Technology, China. Her current research interests include face recognition and sparse learning.

Jianchun Zhang received the MS degree in computer science and application from Nanjing University of Aeronautics and Astronautics, China in 2010. His research interests include pattern recognition and image processing.

Zhisong Pan received the BS degree in computer science and MS degree in computer science and application from PLA Information Engineering University, China, in 1991 and 1994 respectively, and the PhD degree in Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, China in 2003. From July 2006 to the present, he has led several key projects of intelligent data processing for the network management. His current research interests mainly include pattern recognition, machine learning and neural networks.

Daoqiang Zhang received the BS and PhD degrees in computer science from Nanjing University of Aeronautics and Astronautics (NUAA), China in 1999 and 2004, respectively. He is currently a professor in the Department of Computer Science and Engineering of NUAA. His research interests include machine learning, pattern recognition, data mining, and image processing. In these areas, he has published over 40 technical papers in refereed international journals or conference proceedings. He was nominated for the National Excellent Doctoral Dissertation Award of China in 2006, and won the best paper award at the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI’06). He has served as a program committee member for several international and native conferences. He is also a member of Chinese Association of Artificial Intelligence (CAAI) Machine Learning Society.

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Zhang, Y., Zhang, J., Pan, Z. et al. Multi-view dimensionality reduction via canonical random correlation analysis. Front. Comput. Sci. 10, 856–869 (2016). https://doi.org/10.1007/s11704-015-4538-7

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  • DOI: https://doi.org/10.1007/s11704-015-4538-7

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