Relative Principle Component and Relative Principle Component Analysis Algorithm

  • Authors:

  • Cheng-Lin Wen;Jing Hu;Tian-Zhen Wang

  • Affiliations:

  • Institute of Information and Control, Hangzhou Dianzi University, 310018 Hangzhou, China;Department of Computer and Information Engineering, Henan University, 475001 Kaifeng, China;Department of Electrical Automation, Shanghai Maritime University, 200135 Shanghai, China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
  • Year:
  • 2007

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Abstract

Aiming at the problems happened in the practical application of traditional Principle Component Analysis (PCA), the concept of Relative Principle Component (RPC) and method of Relative Principle Component Analysis (RPCA) are put forward. Meanwhile, some concepts such as Relative Transform (RT), "Rotundity" Scatter and so on are introduced. The new algorithm can overcome some disadvantages of traditional PCA for compressing data when data is "Rotundity" Scatter. A simulation has been used to demonstrate the effectiveness and practicability of the algorithm proposed. The RPCs selected by RPCA are more representative, and the way to choose RPCs is more flexible, so that the application of the new algorithm will be very extensive.