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Dynamic time warping constraint learning for large margin nearest neighbor classification

Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification,... Full description

Journal Title: Information Sciences 2011, Vol.181(13), pp.2787-2796
Main Author: Yu, Daren
Other Authors: Yu, Xiao , Hu, Qinghua , Liu, Jinfu , Wu, Anqi
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0020-0255 ; E-ISSN: 1872-6291 ; DOI: 10.1016/j.ins.2011.03.001
Link: https://www.sciencedirect.com/science/article/pii/S0020025511001204
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recordid: elsevier_sdoi_10_1016_j_ins_2011_03_001
title: Dynamic time warping constraint learning for large margin nearest neighbor classification
format: Article
creator:
  • Yu, Daren
  • Yu, Xiao
  • Hu, Qinghua
  • Liu, Jinfu
  • Wu, Anqi
subjects:
  • Time Series Classification
  • Dynamic Time Warping
  • Constraint Learning
  • Large Margin
  • Engineering
  • Library & Information Science
ispartof: Information Sciences, 2011, Vol.181(13), pp.2787-2796
description: Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method.
language: eng
source:
identifier: ISSN: 0020-0255 ; E-ISSN: 1872-6291 ; DOI: 10.1016/j.ins.2011.03.001
fulltext: fulltext
issn:
  • 0020-0255
  • 00200255
  • 1872-6291
  • 18726291
url: Link


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subjectTime Series Classification ; Dynamic Time Warping ; Constraint Learning ; Large Margin ; Engineering ; Library & Information Science
descriptionNearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method.
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Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method.

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Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by maximizing the nearest neighbor hypothesis margin. In addition, a reduction technique is combined with a search process to condense the prototypes. The approach is implemented and tested on UCR datasets. Experimental results show the effectiveness of the proposed method.

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