Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 202-205.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Graph-based Ratio Cut Model for Classification of High-dimensional Data and Fast Algorithm

ZHENG Shi-xiu1,2,PAN Zhen-kuan1,XU Zhi-lei1   

  1. College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China1
    Business School,Qingdao University,Qingdao,Shandong 266071,China2
  • Online:2018-06-20 Published:2018-08-03

Abstract: Data classification is an important part of data mining.With the increase of the amount of data and the dimension of data,the processing of large-scale and high-dimensional data becomes the key problem.In order to improve the accuracy of data classification,inspired by the image segmentation algorithm in computer vision,an algorithm based on nonlocal operator was proposed for the classic Ratio Cut classification model.A new energy functional is modeled by introducing Lagrange multipliers,and the energy functional is solved by the alternating optimization method.Numerical experiments show that the accuracy and computational efficiency of the proposed algorithm are greatly improved compared with the traditional classification method.

Key words: Graph, Nonlocal means, Ratio Cut, Data classification

CLC Number: 

  • TP391
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