粗視化された相対近傍グラフによる大規模パターン集合の分布構造の可視化

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: 2017/11/01

タイトル(英語): Visualizing the Distribution of a Large-Scale Pattern Set using Compressed Relative Neighborhood Graph

著者名: 後藤　雅典（グローリー（株）研究開発センター），石田　良介（グローリー（株）研究開発センター），内田　誠一（九州大学大学院システム情報科学研究院）

著者名(英語): Masanori Goto (Research & Development Center, GLORY LTD.), Ryosuke Ishida (Research & Development Center, GLORY LTD.), Seiichi Uchida (Faculty of Information Science and Electrical Engineering,Kyushu University)

キーワード: 大規模パターン集合認識，多クラス識別，相対近傍グラフ，分布解析，ネットワーク解析　　Large-scale pattern recognition，Multi-class pattern recognition，Relative neighborhood graph，Distribution analysis，Network analysis

要約(英語): The goal of this research is to understand the true distribution of character patterns. Advances in computer technology for mass storage and digital processing have paved way to process a massive dataset for various pattern recognition problems. If we can represent and analyze the distribution of a large-scale pattern set directly and understand its relationships deeply, it should be helpful for improving classifier for pattern recognition. For this purpose, we use a visualization method to represent the distribution of patterns using a relative neighborhood graph (RNG), where each node corresponds to a single pattern. Specifically, we visualize the pattern distribution using a compressed representation of RNG (Clustered-RNG). Clustered-RNG can visualize inter-class relationships (e.g. neighboring relationships and overlaps of pattern distribution among “multiple classes”) and it represents the distribution of the patterns without any assumption, approximation or loss. Through large-scale printed and handwritten digit pattern experiments, we show the properties and validity of the visualization using Clustered-RNG.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.137 No.11 （2017） 特集：電気関係学会関西連合大会

本誌掲載ページ: 1495-1505 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/137/11/137_1495/_article/-char/ja/