Earticle

초록

영어

One-class support vector machine is an important and efficient classifier which is used in the situation that only one class of data is available, and the other is too expensive or difficult to collect. It uses vector as input data, and trains a linear or nonlinear decision function in vector space. However, there is reason to consider data as tensor. Tensor representation can make use of the structural information present in the data, which cannot be handled by the traditional vector based classifier. The significant benefit of using tensor as input is the reduction of the number of decision parameters, which can avoid the overfitting problems and especially suitable for small sample and large dimension cases. In this paper we have proposed a tensor based one-class classification algorithm named linear one-class support tensor machine. It aims to find a hyperplane in tensor space with maximal margin from the origin that contains almost all the data of the target class. We demonstrate the performance of the new tensor based classifier on several publicly available datasets in comparison with the standard linear one-class support vector machine. The experimental results indicate the validity and advantage of our tensor based classifier.

목차

Abstract
 1. Introduction
 2. Reviews of Relevant Research
  2.1. Support Tensor Machine
  2.2. One-Class Support Vector Machine
 3. One-Class Support Tensor Machine
 4. Experimental Evaluation
  4.1. Classification Performance
  4.2. Parameter Sensitivity
 5. Conclusions and Future Work
 Acknowledgments
 References

키워드

저자

• Yanyan Chen [ College of science, China Agricultural University,Beijing,100083,China, College of Applied Science and Technology,Beijing Union University,Beijing 102200,China ]
• Ping Zhong [ College of science, China Agricultural University,Beijing,100083,China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605