The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Process Data Analytics via Probabilistic Latent Variable Models: A Tutorial Review
Abstract
Dimensionality reduction is important for the high-dimensional nature of data in the process industry, which has made latent variable modeling methods popular in recent years. By projecting high-dimensional data into a lower-dimensional space, latent variables models are able to extract key information from process data while simultaneously improving the efficiency of data analytics. Through a probabilistic viewpoint, this paper carries out a tutorial review of probabilistic latent variable models on process data analytics. Detailed illustrations of different kinds of basic probabilistic latent variable models (PLVM) are provided, as well as their research statuses. Additionally, more counterparts of those basic PLVMs are introduced and discussed for process data analytics. Several perspectives are highlighted for future research on this topic.
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Cites
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017) · IEEE Access · doi:10.1109/access.2017.2756872 · Cited by 922
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Cited by
Zhu, J., Ge, Z., Song, Z., & Gao, F. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.003 · Cited by 308
Self-citationState-of-health prediction for lithium-ion batteries with multiple gaussian process regression model
Zheng, X., & Deng, X. (2019) · IEEE Access · doi:10.1109/access.2019.2947294 · Cited by 74
Rethinking the value of just-in-time learning in the era of industrial big data
Yang, Z., & Ge, Z. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3073645 · Cited by 37
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Metadata
- Authors
- Ge, Z.
- Venue
- Industrial & Engineering Chemistry Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-08-31
- Volume
- 57
- Issue
- 38
- Pages
- 12646-12661
- ISSN
- 0888-5885 (ISSN-L)
- 1520-5045
- OpenAlex
- W2889017122
- Cited by
- 217
- References
- 2
- FWCI
- 23.27
- Type
- journal article
- Language
- en
- Open access
- closed
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