Summary of Dpgiil: Dirichlet Process-deep Generative Model-integrated Incremental Learning For Clustering in Transmissibility-based Online Structural Anomaly Detection, by Lin-feng Mei and Wang-ji Yan
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection
by Lin-Feng Mei, Wang-Ji Yan
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach called Dirichlet process-deep generative model-integrated incremental learning (DPGIIL) that combines the strengths of deep generative models (DGMs) and Dirichlet process mixture models (DPMMs) to cluster vibration responses, such as transmissibility functions. The DPGIIL framework leverages variational Bayesian inference to jointly optimize DGM and DPMM parameters, allowing for feature extraction regularization by the DPMM. A greedy split-merge scheme-based coordinate ascent variational inference method is used to accelerate optimization. The framework is illustrated using a variational autoencoder (VAE) as an example, but can be adapted to other DGMs. Case studies demonstrate that DPGIIL outperforms some state-of-the-art approaches in structural anomaly detection and clustering, while also generating new clusters online. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to group vibration responses using computers. It combines two special kinds of computer programs: deep generative models (which are good at finding patterns) and Dirichlet process mixture models (which are good at identifying different groups). This combined approach is called DPGIIL. It helps find the right number of groups and handle a lot of data all at once. The new method can also learn from previous data to make better predictions in the future. |
Keywords
» Artificial intelligence » Anomaly detection » Bayesian inference » Clustering » Feature extraction » Generative model » Inference » Optimization » Regularization » Variational autoencoder
