The DOI matches its Crossref record, with a small difference in title or year.
Split and merge EM algorithm for improving Gaussian mixture density estimates
Abstract
We present a split and merge EM algorithm to overcome the local maximum problem in Gaussian mixture density estimation. Nonglobal maxims often involve having too many Gaussians in one part of the space and too few in another, widely separated part of the space. To escape from such configurations we repeatedly perform split and merge operations using a new criterion for efficiently selecting the split and merge candidates. Experimental results on synthetic and real data show the effectiveness of using the split and merge operations to improve the likelihood of both the training data and of held-out test data.
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An adapted transformer network can be used for anomaly localization using Gaussian Approximation of the latent features.
“In our work we show how an adapted transformer network can be used for anomaly localization using Gaussian Approximation [10], [11] of the latent features and also how different configurations can be tweaked to win some of the shortcomings of the vision transformer network.”
From VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization (2021) · cites this paper as [11]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 8
- rabiner, 1993, Fundamentals of speech recognition
- ormoneit, 1996, Advances in neural information processing systems, 8, 542
- ueda, 1995, Advances in neural information processing systems, 7, 545
- dempster, 1977, Maximum likelihood from incomplete data via the EM algorithm, Journal of Royal Statistical Society B, 39, 1
- 10.1016/0893-6080(94)90003-5 · DOI
- 10.1016/S0893-6080(97)00133-0 · DOI
- kambhatla, 1995, Advances in neural information processing systems, 7, 681
- maclachlan, 1988, Mixture Models Inference and Application to Clustering
Cited by
Sorted by citations
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization
Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., & Foresti, G. L. (2021) · 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie45552.2021.9576231 · Cited by 462
“In our work we show how an adapted transformer network can be used for anomaly localization using Gaussian Approximation [10], [11] of the latent features and also how different configurations can be tweaked to win some of the shortcomings of the vision transformer network.”
Method
Metadata
- Authors
- Ueda, N., Nakano, R., Ghahramani, Z., & Hinton, G. E.
- Affiliations
- NTT (Japan) (JP)
- University of Toronto (CA)
- Venue
- Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378)
- Publisher
- IEEE
- Published
- 2002-11-27
- Pages
- 274-283
- ISSN
- 1551-2541 (ISSN-L)
- OpenAlex
- W3017026857
- Cited by
- 23
- References
- 0
- Citations by year
- 2024: 1 · 2023: 1 · 2021: 1 · 2019: 2 · 2018: 2 · 2016: 1 · 2015: 1 · 2014: 2 · 2013: 3 · 2012: 1
- FWCI
- 1.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Machine learning for signal processing ... (publishedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- Gaussian mixture model, density estimation, model selection, likelihood maximization
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