Comparison of Pattern Extraction Capability between Self-Organizing Maps and Principal Component Analysis

• 井芹, 慶彦

  九州大学大学院工学府環境都市部門

• 松浦, 知徳

  富山大学大学院理工学研究部 : 教授

• 飯塚, 聡

  防災科学技術研究所

• 西山, 浩司

  九州大学大学院工学研究院環境都市部門 : 助教

• 神野, 健二

  九州大学大学院工学研究院環境都市部門 : 教授

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.

収録刊行物

• 九州大学工学紀要

  九州大学工学紀要 69 (2), 37-47, 2009-06-22

  九州大学大学院工学研究院

キーワード

• SOM
• PCA
• Simulation
• Pattern extraction
• Comparison

詳細情報

• CRID: 1050861482659256576
• NII論文ID: 120001390916
• NII書誌ID: AA11481788
• HANDLE: 2324/14898
• 本文言語コード: en
• 資料種別: departmental bulletin paper
• データソース種別:
  • IRDB
  • CiNii Articles
  • KAKEN