ベクトル量子化モデルによるPeculiarity Factorの近似計算

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2015/03/01

タイトル(英語): A Method of Peculiarity Factor Approximation using Vector Quantization Model

著者名: 齊藤　史哲（青山学院大学理工学部経営システム工学科），石津　昌平（青山学院大学理工学部経営システム工学科）

著者名(英語): Fumiaki Saitoh (Department of Industrial and Systems Engineering, Aoyama Gakuin University), Syohei Ishizu (Department of Industrial and Systems Engineering, Aoyama Gakuin University)

キーワード: ベクトル量子化，Peculiarity Factor(PF)，競合学習，データマイニング，異常検知，計算量抑制　　Vector Quantization，Peculiarity Factor (PF)，Competitive Learning，Data mining，Anomaly Detection，Computational Reduction

要約(英語): The purpose of this research is to reduce the computational complexity of the Peculiarity Factor (PF). Recently, PF has been adopted as the index for anomaly data detection, and it is widely used in various mining scenes. The fact that PF has become a powerful mining tool is positive because its calculation method is extremely simple and the results of the calculation are easy to understand visually. One of the most important problems for using PF for large-scale data is the rapidly increasing computational complexity required when the data volume increases. The computational complexity of PF is in the polynomial order because the PF of each data is calculated distantly over all the data. In this study, we propose an approximation methodology for PF for computational reduction and for enhanced robustness using the vector quantization model. Approximate values of PF are calculated by replacing the actual data with the nodes of vector quantization model. By calculating PF based on the vector quantization node vectors, we achieve restraint in the increasing computational complexity.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.3 （2015）

本誌掲載ページ: 304-311 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/135/3/135_304/_article/-char/ja/