Consistent and Specific Multi-view Relative-Transform Classification

  • Siyuan Ping
  • Long Zhang
  • Xing Wang
  • Guoxian YuEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1072)


In many practical problems, the same objects can be described in many different ways or from different angles. These multiple descriptions constitute multiple views of objects. Multi-view classification methods try to exploit information from all views to improve the classification performance and reduce the effect of noises. However, how to efficiently exploit the consistency and specificity in multiple views remains a challenge. In addition, it is also worth to explore the processing results of multi-view data more inline with human cognition. For this reason, we propose a new multi-view classification algorithm, Consistent and Specific Multi-View Relative-transform Classification (CSMRtC). CSMRtC firstly explores the underlying subspace structure of different views exhaustively, to evacuate consistency and specificity of multi-view data. Next, these data matrices are processed using the relative transform technique. As for the consistency and specificity, consistent matrix stores the shared information of multiple data matrices, specificity captures the characteristic of each view. Then, we use the relative transformations to transform data from raw space to relative spaces, to achieve the purpose of suppress noise in the data and improve the distinction between the data. Comprehensive evaluations with several state-of-the-art competitors demonstrate the efficiency and the superiority of the proposed method.


Multi-view learning Classification Relative transformation 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Computer and Information SciencesSouthwest UniversityChongqingChina

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