抄録
We propose the use of a novel feature, called local distance features, for time series classification. The local distance features are extracted using Dynamic Time Warping (DTW) and classified using Convolutional Neural Networks (CNN). DTW is classically as a robust distance measure for distance-based time series recognition methods. However, by using DTW strictly as a global distance measure, information about the matching is discarded. We show that this information can further be used as supplementary input information in temporal CNNs. This is done by using both the raw data and the features extracted from DTW in multi-modal fusion CNNs. Furthermore, we explore the effects of different prototype selection methods, prototype numbers, and data fusion schemes induce on the accuracy. We perform experiments on a wide range of time series datasets including three Unipen handwriting datasets, four UCI Machine Learning Repository datasets, and 85 UCR Time Series Classification Archive datasets.
| 元の言語 | 英語 |
|---|---|
| 記事番号 | 107024 |
| ジャーナル | Pattern Recognition |
| 巻 | 97 |
| DOI | |
| 出版物ステータス | 出版済み - 1 1 2020 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
これを引用
Time series classification using local distance-based features in multi-modal fusion networks. / Kenji Iwana, Brian; Uchida, Seiichi.
：: Pattern Recognition, 巻 97, 107024, 01.01.2020.研究成果: ジャーナルへの寄稿 › 記事
}
TY - JOUR
T1 - Time series classification using local distance-based features in multi-modal fusion networks
AU - Kenji Iwana, Brian
AU - Uchida, Seiichi
PY - 2020/1/1
Y1 - 2020/1/1
N2 - We propose the use of a novel feature, called local distance features, for time series classification. The local distance features are extracted using Dynamic Time Warping (DTW) and classified using Convolutional Neural Networks (CNN). DTW is classically as a robust distance measure for distance-based time series recognition methods. However, by using DTW strictly as a global distance measure, information about the matching is discarded. We show that this information can further be used as supplementary input information in temporal CNNs. This is done by using both the raw data and the features extracted from DTW in multi-modal fusion CNNs. Furthermore, we explore the effects of different prototype selection methods, prototype numbers, and data fusion schemes induce on the accuracy. We perform experiments on a wide range of time series datasets including three Unipen handwriting datasets, four UCI Machine Learning Repository datasets, and 85 UCR Time Series Classification Archive datasets.
AB - We propose the use of a novel feature, called local distance features, for time series classification. The local distance features are extracted using Dynamic Time Warping (DTW) and classified using Convolutional Neural Networks (CNN). DTW is classically as a robust distance measure for distance-based time series recognition methods. However, by using DTW strictly as a global distance measure, information about the matching is discarded. We show that this information can further be used as supplementary input information in temporal CNNs. This is done by using both the raw data and the features extracted from DTW in multi-modal fusion CNNs. Furthermore, we explore the effects of different prototype selection methods, prototype numbers, and data fusion schemes induce on the accuracy. We perform experiments on a wide range of time series datasets including three Unipen handwriting datasets, four UCI Machine Learning Repository datasets, and 85 UCR Time Series Classification Archive datasets.
UR - http://www.scopus.com/inward/record.url?scp=85071504668&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071504668&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2019.107024
DO - 10.1016/j.patcog.2019.107024
M3 - Article
AN - SCOPUS:85071504668
VL - 97
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
M1 - 107024
ER -