Efficient temporal pattern recognition by means of dissimilarity space embedding with discriminative prototypes. (April 2017)

• Record Type: Journal Article
• Authors: Iwana, Brian Kenji; Frinken, Volkmar; Riesen, Kaspar; Uchida, Seiichi
• Is Part Of: Pattern recognition. Volume 64(2017:Apr.)
• Page Start: 268
• Page End: 276
• Publication Date: 2017-04
• Subjects: Temporal patterns -- Online digit classification -- Dissimilarity representation -- Ensemble classification -- Dissimilarity space embedding; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.11.013
• Languages: English
• ISSNs: 0031-3203

Abstract

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. Abstract : Graphical abstract: Abstract : Highlights: We propose a method of using dissimilarity space embedding for temporal patterns. Ensemble classification was used for prototype selection to increase the efficiency. We evaluate the performance of our method on online handwritten digits. The experiments showed a high accuracy with a very small number of patterns.