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

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

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)268-276
Number of pages9
JournalPattern Recognition
Volume64
DOIs
Publication statusPublished - Apr 1 2017

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Pattern recognition
Vector spaces
Feature extraction
Topology
Costs

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

Efficient temporal pattern recognition by means of dissimilarity space embedding with discriminative prototypes. / Iwana, Brian Kenji; Frinken, Volkmar; Riesen, Kaspar; Uchida, Seiichi.

In: Pattern Recognition, Vol. 64, 01.04.2017, p. 268-276.

Research output: Contribution to journalArticle

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