End-to-end deep representation learning for time series clustering: a comparative study
type: Item
instance of: scholarly article
title: End-to-end deep representation learning for time series clustering: a comparative study
zbMATH Open document ID: 1494.68242
author: Baptiste Lafabregue
author: Jonathan Weber
author: Pierre Gançarski
author: Germain Forestier
published in: Data Mining and Knowledge Discovery
publication date: 2022-03-25T00:00:00Z
Mathematics Subject Classification ID: 68T07
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 7498455
zbMATH Keywords: clustering
zbMATH Keywords: deep learning
zbMATH Keywords: time series
describes a project that uses: ImageNet
describes a project that uses: rocket
describes a project that uses: CBAM
describes a project that uses: ClusterGAN
describes a project that uses: AlexNet
describes a project that uses: FaceNet
describes a project that uses: Tensor2Tensor
describes a project that uses: UMAP
describes a project that uses: Grad-CAM
describes a project that uses: t-SNE
MaRDI profile type: Publication
full work available at URL: s10618-021-00796-y
OpenAlex ID: W3205581618
Wikidata QID: Q125121836
cites work: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
cites work: Q3093335
cites work: Deep learning for time series classification: a review
cites work: Q4187195
cites work: Neural networks and physical systems with emergent collective computational abilities.
cites work: Clustering of time series data -- a survey
cites work: Q3096207
cites work: Q4809022
cites work: Fast and Accurate Time-Series Clustering
cites work: A global averaging method for dynamic time warping, with applications to clustering
cites work: Learning representations by back-propagating errors
cites work: Dynamic programming algorithm optimization for spoken word recognition
DOI: 10.1007/S10618-021-00796-Y
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description: scientific article; zbMATH DE number 7498455