ISSN 0253-2778

CN 34-1054/N

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Prototype based relative attribute learning

  • 1.

    School of Mechanical Engineering, Yancheng Institute of Technology, Yancheng 224051, China

  • 2.

    Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Cite this: JUSTC, 2017, 47(1): 32-39
https://doi.org/10.3969/j.issn.0253-2778.2017.01.005
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  • Received Date: February 29, 2016
  • Revised Date: September 16, 2016
  • Published Date: January 30, 2017
    Abstract
  • According to the research on representation learning, a proper feature representation of data has a greater impact than classifiers on classification. It’s almost become the most important part in system design. In this paper, based on prototype theorem in psychology, a new feature is proposed. Specifically, the prototype dataset is composed of representative data of extra datasets. Then, the rank functions are derived based on the relationship between the prototype dataset and any data set. Thus, any data could be represented via the rank functions and the values of the functions are their new features. The proposed method has been checked on the MINST database and Pubfig database. Compared with the gray-scale feature and attribute, the prototype based relative attribute is more reasonable and has better performance.
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