The Journal of Korea Institute of Information, Electronics, and Communication Technology (한국정보전자통신기술학회논문지)

Korea Information Electronic Communication Technology (한국정보전자통신기술학회)
권호 표지
DOI QR코드

DOI QR Code

Design of Regression Model and Pattern Classifier by Using Principal Component Analysis

주성분 분석법을 이용한 회귀다항식 기반 모델 및 패턴 분류기 설계

  • Roh, Seok-Beom (Department of Electrical & Electronic Eng. Joongbu University) ;
  • Lee, Dong-Yoon (Department of Electrical & Electronic Eng. Joongbu University)
  • Received : 2017.12.01
  • Accepted : 2017.12.12
  • Published : 2017.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

The new design methodology of prediction model and pattern classification, which is based on the dimension reduction algorithm called principal component analysis, is introduced in this paper. Principal component analysis is one of dimension reduction techniques which are used to reduce the dimension of the input space and extract some good features from the original input variables. The extracted input variables are applied to the prediction model and pattern classifier as the input variables. The introduced prediction model and pattern classifier are based on the very simple regression which is the key point of the paper. The structural simplicity of the prediction model and pattern classifier leads to reducing the over-fitting problem. In order to validate the proposed prediction model and pattern classifier, several machine learning data sets are used.

본 논문에서는 매우 높은 차원을 가진 데이터에서 의미 있는 특징 벡터 추출하여 입력 공간의 차원을 줄이기 위하여 주성분 분석법을 사용하였다. 주성분 분석법을 이용하여 축소된 차원을 가진 입력 데이터를 이용하여 회귀 다항식의 입력벡터로 사용하는 모델과 패턴 분류기의 설계 방법을 제안하였다. 제안된 모델 및 패턴 분류기는 매우 단순한 구조를 가진 회귀다항식을 기반으로 설계하여 모델 및 패턴 분류기의 과적합 문제를 해결 하고자 하였다. 제안된 설계방법을 적용하여 설계된 모델과 패턴 분류기의 성능을 비교 및 평가하기 위하여, 다양한 기계 학습 데이터 집합을 사용하였다.

Keywords

References

  1. J. T. Seong, "Analysis of Signal Recovery for Compressed Sensing using Deep Learning Technique," The Korea Institute of Information & Electronic Communication Technology, Vol. 10 , no. 4, pp. 257-267, 2017. https://doi.org/10.17661/jkiiect.2017.10.4.257
  2. I.-H. Lee, T.-S. Choi, "Shape from focus algorithm with optimization of focus measure for cell image," The Korea Institute of Information & Electronic Communication Technology, Vol. 3, pp. 8-13, 2010.
  3. E. H. Jeong, and B. K. Lee, "A Design of Customized Market Analysis Scheme Using SVM and Collaboration Filtering Scheme," The Korea Institute of Information & Electronic Communication Technology, vol. 9, no. 6, pp. 609-616, 2016. https://doi.org/10.17661/jkiiect.2016.9.6.609
  4. J. H. Son, S. Y. Kim, "Texture Classification Based on Gabor-like Feature," The Korea Institute of Information & Electronic Communication Technology, vol. 10, no. 2, 147-153, 2017. https://doi.org/10.17661/jkiiect.2017.10.2.147
  5. G. B. Hwang, Q. U. Zhu, C. K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, pp. 489-501, 2006. https://doi.org/10.1016/j.neucom.2005.12.126
  6. I. T. Jolliffe, "Principal Component Analysis", second edition, Springer-Verlog, 2002.
  7. M. Imaizumi, and K. Kato, "PCA-based estimation for functional linear regression with functional responses," Journal of multivariate analysis, vol. 163, pp. 15-36, 2018. https://doi.org/10.1016/j.jmva.2017.10.001
  8. M. Lichman, "UCI Machine Learning Repository", 2013, http://archive.ics.uci.edu/ml.