Comparison of pattern extraction capability between self-organizing maps and principal component analysis

Yoshihiko Iseri, Tomonori Matsuura, Satoshi Iizuka, Koji Nishiyama, Kenji Jinno

研究成果: Contribution to journalArticle査読

11 被引用数 (Scopus)

抄録

Principal Component Analysis (PCA) has been extensively used for multivariate data analysis including climate data analysis. However, recent studies suggest the possible use of Self-Organizing Maps (SOM) for climate data analysis. In order to adequately utilize the PCA and SOM, it is crucial to clarify the difference of both methods. This study compared the pattern extraction capability of SOM and PCA in order to clarify the difference of both methods. Comparisons of the methods were performed by conducting two kinds of simulations. The first stimulation confirmed SOM can extract non-orthogonal patterns while the patterns extracted by PCA are mutually orthogonal. The second simulation investigated how the patterns extracted by each method would change when new inputs were added. When new inputs were added, PCA did not extract the pattern in the original inputs and additional inputs. On the other hand, SOM extracted the pattern in original and additional inputs.

本文言語英語
ページ(範囲)37-47
ページ数11
ジャーナルMemoirs of the Faculty of Engineering, Kyushu University
69
2
出版ステータス出版済み - 6 1 2009
外部発表はい

All Science Journal Classification (ASJC) codes

  • エネルギー(全般)
  • 大気科学
  • 地球惑星科学(全般)
  • 技術マネージメントおよび技術革新管理

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