Journal
PATTERN RECOGNITION
Volume 64, Issue -, Pages 268-276Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2016.11.013
Keywords
Temporal patterns; Online digit classification; Dissimilarity representation; Ensemble classification; Dissimilarity space embedding
Funding
- Grants-in-Aid for Scientific Research [26240024] Funding Source: KAKEN
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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 tithe 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.
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