DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection
cs.LG, physics.data-an, stat.ML
Submitted: 2024-12-06
Updated: 2025-10-08
Comments: 53 pages,9 figures,7 tables, accepted by Advanced Engineering Informatics
DOI: 10.1016/j.aei.2025.103926
Code: https://github.com/Christine-cmd/DPGIIL
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems
- Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
- A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
- Stick-Breaking Variational Autoencoders
- DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder
- Auto-Encoding Variational Bayes
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