Robustness of Gaussian Mixture Reduction for Split-and-Conquer Learning of Finite Gaussian Mixtures Article Swipe
Qiong Zhang
,
Jiahua Chen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.11159/icsta21.135
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.11159/icsta21.135
In the era of big data, there is an increasing demand for split-and-conquer learning of finite mixture models.Recent work [1] proposes several split-and-conquer approaches for learning finite Gaussian mixtures and they are found to be both statistically and computationally efficient when the order of the mixture is correctly specified.Due to the nature of mixture models, correctly specifying the order of mixture on local machines can be an unrealistic assumption.In this paper, we evaluate the performance of several split-andconquer learning approaches, both when the order is correct and when it is over-specified on the local machines, based on simulations.We find that there is a trade-off between robustness and computational efficiency: the computationally intensive approach is robust against over-specification, while the two computationally friendly approaches have compromised statistical performance when the order is over-specified.The results suggest that the information in the data about the true distribution is not lost in the split step of the learning, and aggregation strategies must be developed in a computationally and statistically efficient way.
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- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.11159/icsta21.135
- https://doi.org/10.11159/icsta21.135
- OA Status
- bronze
- References
- 18
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- 10
- OpenAlex ID
- https://openalex.org/W3195273118
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https://openalex.org/W3195273118Canonical identifier for this work in OpenAlex
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https://doi.org/10.11159/icsta21.135Digital Object Identifier
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Robustness of Gaussian Mixture Reduction for Split-and-Conquer Learning of Finite Gaussian MixturesWork title
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articleOpenAlex work type
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enPrimary language
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2021Year of publication
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2021-08-01Full publication date if available
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Qiong Zhang, Jiahua ChenList of authors in order
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https://doi.org/10.11159/icsta21.135Direct link to full text PDF
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bronzeOpen access status per OpenAlex
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https://doi.org/10.11159/icsta21.135Direct OA link when available
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Robustness (evolution), Gaussian, Divide and conquer algorithms, Gaussian process, Computer science, Reduction (mathematics), Mathematics, Algorithm, Applied mathematics, Computational chemistry, Chemistry, Geometry, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
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| citation_normalized_percentile.value | 0.07963446 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |