Earticle

초록

영어

As traditional Linear Discriminant Analysis algorithm runs slowly in large data set, this paper proposed a fast LDA algorithm based on active learning. In the proposed algorithm, the original training set is divided into three parts, i.e. initial training set, correction set and testing set. Secondly, LDA algorithm is running on the initial training set, and the projection vector can be obtained. Thirdly, we select from correction set the samples whose projection is farthest from the mean vector, add them into the initial training set and compute the projection vector again. Repeat this step until the classification precision attains the expected target or the correction set is empty. The simulation experiments on the UCI data set and the MNIST dataset show that the proposed algorithm running fast on large data set, and has a good classification precision.

목차

Abstract
 1. Introduction
 2. Review on Active Learning and the LDAAlgorithm
  2.1. Review on Active Learning
  2.2 Review on the LDAalgorithm
 3. Algorithm Design
 4. Experiments
  4.1. Experiments on the UCI Data Sets
  4.2. Experiments on the MNIST Data Set
  4.3. The Experimental Result and Analysis
 5. Conclusion
 References

키워드

저자

• Xu Yu [ School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, China ]
• Yan-ping Zhou [ School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, China ]
• Chun-nian Ren [ School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605