The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Unsupervised learning of finite mixture models
Abstract
This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective "unsupervised" is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectation-maximization (EM) algorithm, it does not require careful initialization. The proposed method also avoids another drawback of EM for mixture fitting: the possibility of convergence toward a singular estimate at the boundary of the parameter space. The novelty of our approach is that we do not use a model selection criterion to choose one among a set of preestimated candidate models; instead, we seamlessly integrate estimation and model selection in a single algorithm. Our technique can be applied to any type of parametric mixture model for which it is possible to write an EM algorithm; in this paper, we illustrate it with experiments involving Gaussian mixtures. These experiments testify for the good performance of our approach.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Elements of Information Theory
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An entropy criterion for assessing the number of clusters in a mixture model
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A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
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Sphere Packings, Lattices and Groups
Conway, J. H., & Sloane, N. J. A. (1993) · Grundlehren der mathematischen Wissenschaften · doi:10.1007/978-1-4757-2249-9 · Cited by 880
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Nguyen-Tuong, D., & Peters, J. (2011) · Cognitive Processing · doi:10.1007/s10339-011-0404-1 · Cited by 503
A tutorial on task-parameterized movement learning and retrieval
Calinon, S. (2016) · Intelligent Service Robotics · doi:10.1007/s11370-015-0187-9 · Cited by 422
Efficient Greedy Learning of Gaussian Mixture Models
Verbeek, J. J., Vlassis, N., & Kröse, B. (2003) · Neural Computation · doi:10.1162/089976603762553004 · Cited by 340
Abu-Akel, A., Allison, C., Baron-Cohen, S., & Heinke, D. (2019) · Molecular Autism · doi:10.1186/s13229-019-0275-3 · Cited by 109
Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads
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Metadata
- Authors
- Figueiredo, M. A. T., & Jain, A. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002-03
- Volume
- 24
- Issue
- 3
- Pages
- 381-396
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2015245929
- Cited by
- 2,125
- References
- 7
- FWCI
- 59.89
- Type
- journal article
- Language
- en
- Open access
- closed
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