A global averaging method for dynamic time warping, with applications to clustering
By Franccois Petitjean, Alain Ketterlin, Pierre Gançarski. Pattern Recognition, 2011
1,132 citations recorded by the source corpus.
Subfields: Information Systems, Signal Processing
View Paper: https://www.semanticscholar.org/paper/07b0e907ebca7c75ca4b45a044f8292dc8bb5ca2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Luis Gravano
- Lorenzo Bruzzone
- Marco Cuturi
- Seiichi Uchida
- Brian Kenji Iwana
- John Paparrizos
- Claudio Persello
- Raphael T. Haftka
In Signal Processing
- Devis Tuia
- John Paparrizos
- Luis Gravano
- Gerhard Venter
- Lorenzo Bruzzone
- Marco Cuturi
- Seiichi Uchida
- Brian Kenji Iwana
Citations in the corpus
- Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances (2016, methodology): “Beyond classical feature extraction, the authors in [47] align multitemporal sequences based on a measure of similarity between sequence barycenters, which corresponds to a global alignment of the spectra in a time series of images.”
- Soft-DTW: a Differentiable Loss Function for Time-Series (2017, methodology): “In this section, we compare the soft-DTW barycenter approach presented in § 3.1 to DBA (Petitjean et al., 2011) and a simple batch subgradient method.”
- k-Shape: Efficient and Accurate Clustering of Time Series (2015, methodology): “est in querying [2, 19, 45, 46, 48, 62, 74, 79], indexing [9, 13, 41, 42, 44, 77], classification [37, 56, 68, 88], clustering [43, 54, 64, 87, 89], and modeling [4, 38, 86] of such data.”
- Similarity measures for identifying material parameters from hysteresis loops using inverse analysis (2018, methodology): “4 Dynamic Time Warping Dynamic Time Warping (DTW) has been a popular method for pattern recognition, and particularly useful for speech recognition [35, 36].”
- Tslearn, A Machine Learning Toolkit for Time Series Data (2020, background): “…illustrated in the example below and the corresponding Figure 1, where standard Euclidean k -means fails while DTW-based ones (Sakoe and Chiba, 1978; Petitjean et al., 2011; Cuturi and Blondel, 2017) can distinguish between different time series profiles: processing time series data to feed…”
- An empirical survey of data augmentation for time series classification with neural networks (2020, methodology): “Fawaz et al. [46] also used wDBA AS with a ResNet for time series classification.”