Earticle

초록

영어

Recently, a constrained Linear Discriminant Analysis (LDA) algorithm is introduced and gained popularity. However, this algorithm is not applicable in the environment with large amount of data points or when the data point arrive in a sequential manner. In this paper, we aim to propose an incremental version of this algorithm called Incremental Constrained Discriminant Component Analysis (ICDCA) to reduce the computational cost of this algorithm in large datasets. The ICDCA updates the within class scatter matrix and between class scatter matrix instead of calculating it from scratch. This change significantly reduces the computational cost of feature extraction process while keep the accuracy of such features as close as possible to offline version of this algorithm. In the end the effectiveness of ICDCA is compared to other recently proposed incremental LDA. To ensure the reliability of these experiments, they are repeated with several UC I data set. In these comparisons, advantage of ICDCA in the accuracy and speed is demonstrated.

목차

Abstract
 1. Introduction
 2. Preliminary
 3. Related Work
 4. Proposed Method
 5. Experimental Result
  5.1. Setup
  5.2. Experiments on Accuracy
  5.2. Speed Comparison
 6. Conclusion
 References

키워드

저자

• Amin Allahyar [ Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran ]
• Hadi Sadoghi Yazdi [ Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605