Browse > Article

Feature Extraction on High Dimensional Data Using Incremental PCA  

Kim Byung-Joo (영산대학교 네트워크정보공학부)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.8, no.7, 2004 , pp. 1475-1479 More about this Journal
Abstract
High dimensional data requires efficient feature extraction techliques. Though PCA(Principal Component Analysis) is a famous feature extraction method it requires huge memory space and computational cost is high. In this paper we use incremental PCA for feature extraction on high dimensional data. Through experiment we show that proposed method is superior to APEX model.
Keywords
점진적 주성분분석;고유값;고유벡터;고유공간;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Winkeler, J. Manjunath, B.S. and Chandrasekaran, S., 'Subset selection foractive object recognition,' In CVPR, IEEEComputer Society Press, vol. 2, pp.511-516 June 1999
2 http://yann.lecun.com/exdb/mnist/
3 Jolliffe, I.T., Principal component analysis,Springer-Veilag, New York, 1986
4 Chandrasekaran, S., Manjunath, B.S., Wang,Y.F., Winkler, J. and Zhang, H., 'Aneigenspace update algorithm for imageanalysis,' Graphical Models and ImageProcessing, Vo1.59(5), : pp. 321-332, 1997   DOI   ScienceOn
5 Diamantaras, K.I. and Kung, S.Y, PrindpalComponent Neural Networks: Theory andApplications, New York John Wiley&Sons,Inc. 1996
6 Diamantaras, K.I. and Kung, S.Y, Principal Component Neural Networks: Theory and Applications, New York John Wiley&Sons,Inc. 1996
7 Scholkopf, B., Smola, A. and Muller, K.R :Nonlinear component analysis as a kerneleigenvalue problem. Neural Computation10(5), (1998) 1299-1319   DOI   ScienceOn