Efficient temporal pattern recognition by means of dissimilarity space embedding with discriminative prototypes

Brian Kenji Iwana, Volkmar Frinken, Kaspar Riesen, Seiichi Uchida

研究成果: Contribution to journalArticle査読

13 被引用数 (Scopus)

抄録

Dissimilarity space embedding (DSE) presents a method of representing data as vectors of dissimilarities. This representation is interesting for its ability to use a dissimilarity measure to embed various patterns (e.g. graph patterns with different topology and temporal patterns with different lengths) into a vector space. The method proposed in this paper uses a dynamic time warping (DTW) based DSE for the purpose of the classification of massive sets of temporal patterns. However, using large data sets introduces the problem of requiring a high computational cost. To address this, we consider a prototype selection approach. A vector space created by DSE offers us the ability to treat its independent dimensions as features allowing for the use of feature selection. The proposed method exploits this and reduces the number of prototypes required for accurate classification. To validate the proposed method we use two-class classification on a data set of handwritten on-line numerical digits. We show that by using DSE with ensemble classification, high accuracy classification is possible with very few prototypes.

本文言語英語
ページ(範囲)268-276
ページ数9
ジャーナルPattern Recognition
64
DOI
出版ステータス出版済み - 4 1 2017

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 信号処理
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能

フィンガープリント

「Efficient temporal pattern recognition by means of dissimilarity space embedding with discriminative prototypes」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル