VMGAE combines WDTW-based graph construction with a Gaussian-mixture-regularized variational graph autoencoder and reports the best average NMI and RI on 19 UCR time series datasets.
S.; Geethanjali, N.; and Satyanarayana, B
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework
VMGAE combines WDTW-based graph construction with a Gaussian-mixture-regularized variational graph autoencoder and reports the best average NMI and RI on 19 UCR time series datasets.