Earticle

초록

영어

One-class support vector machine (OCSVM) is an important and efficient classifier used when only one class of data is available while others are too expensive or difficult to collect. It uses vector as input data, and trains a linear or nonlinear decision function in vector space. However, the traditional vector-based classifiers may fail when input is matrix. Therefore, it makes sense to study matrixzed classifiers which can make use of the structural information presented in the data. In this paper we propose a matrix-based one-class classification algorithm named Kernel-based Matrixzed One-class Support Vector Machine (KMatOCSVM). It aims to convert the OCSVM to suit for matrix representation data and to deal with nonlinear one-class classification problems. The efficiency and validity of the proposed method is illustrated by four real-world matrix-based human face datasets.

목차

Abstract
 1. Introduction
 2. Relevant Researches
  2.1. One-Class Support Vector Machine
  2.2. Matrixized One-Class Support Vector Machine
 3. Kernel-based Matrixized One-Class Support Vector Machine
 4. Experimental Evaluation
  4.1. Description of Datasets
  4.2. Preparation of Experiments
  4.3. Classification Performance
 5. Conclusions and Future Work
 References

키워드

저자

• Yanyan Chen [ College of Applied Science and Technology, Beijing Union University, Beijing, China ] Corresponding Author
• June Yuan [ College of Applied Science and Technology, Beijing Union University, Beijing, China ]
• Zhengkun Hu [ College of Applied Science and Technology, Beijing Union University, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605