非線形写像による高次元センサ情報の可視化とクラス構造の解析

カテゴリ: 論文誌(論文単位)

グループ名: 【E】センサ・マイクロマシン部門

発行日: 2018/04/01

タイトル(英語): Visualization of High-dimensional Sensor Data and Analysis of their Class Structure Using Nonlinear Map

著者名: 佐藤　仁樹（公立はこだて未来大学 システム情報科学部），佐藤　雅子（情報ノ宮蕗の下工房），高尾　佳史（菊正宗酒造(株)総合研究所）

著者名(英語): Hideki Satoh (School of Systems Information Science, Future University Hakodate), Masako Satoh (Johonomiya Fukinoshita Studio), Yoshifumi Takao (General research laboratory, Kiku-masamune sake brewing Co. Ltd.)

キーワード: 遺伝的アルゴリズム，Nelder-Mead法，基底関数，非線形，写像，可視化　　genetic algorithm，Nelder-Mead method，basis，nonlinear，map，visualization

要約(英語): An algorithm that constructs a nonlinear map from a high-dimensional feature space into a low-dimensional space was developed to enable analysis of the structure of data with high-dimensional characteristic features and their class information obtained using various sensors and analyzers. First, a nonlinear map is defined by summing nonlinear basis functions, and their optimal combination is derived using a genetic algorithm to avoid the “curse of dimensionality.” Next, the coefficients of the basis functions are derived using the Nelder-Mead method to flexibly cope with the various demands for the map that cannot always be expressed using statistics of the characteristic features. As a result, nine-dimensional sake data can be mapped into a two-dimensional space so as not only to discriminate the classes but also to preserve the order of distances between classes as much as possible.

本誌: 電気学会論文誌E（センサ・マイクロマシン部門誌） Vol.138 No.4 （2018）

本誌掲載ページ: 123-131 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/ieejsmas/138/4/138_123/_article/-char/ja/