A novel supervised information feature compression algorithm

  • Authors:

  • Shifei Ding;Zhongzhi Shi

  • Affiliations:

  • College of Information Science and Engineering, Shandong Agricultural University, Taian, P.R. China;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R.China

  • Venue:
  • ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
  • Year:
  • 2006

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Abstract

In this paper, a novel supervised information feature compression algorithm is set up. Firstly, according to the information theories, we carried out analysis for the concept and its properties of the cross entropy, then put forward a kind of lately concept of symmetry cross entropy (SCE), and point out that the SCE is a kind of distance measure, which can be used to measure the difference of two random variables. Secondly, We make the SCE separability criterion of the classes for information feature compression, and design a novel algorithm for information feature compression. At last, the experimental results demonstrate that the algorithm here is valid and reliable, and provides a new research approach for feature compression, data mining and pattern recognition.