Distributed Learning of Finite Gaussian Mixtures
- URL: http://arxiv.org/abs/2010.10412v3
- Date: Wed, 10 Nov 2021 20:55:42 GMT
- Title: Distributed Learning of Finite Gaussian Mixtures
- Authors: Qiong Zhang and Jiahua Chen
- Abstract summary: We study split-and-conquer approaches for the distributed learning of finite Gaussian mixtures.
New estimator is shown to be consistent and retains root-n consistency under some general conditions.
Experiments based on simulated and real-world data show that the proposed split-and-conquer approach has comparable statistical performance with the global estimator.
- Score: 21.652015112462
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Advances in information technology have led to extremely large datasets that
are often kept in different storage centers. Existing statistical methods must
be adapted to overcome the resulting computational obstacles while retaining
statistical validity and efficiency. Split-and-conquer approaches have been
applied in many areas, including quantile processes, regression analysis,
principal eigenspaces, and exponential families. We study split-and-conquer
approaches for the distributed learning of finite Gaussian mixtures. We
recommend a reduction strategy and develop an effective MM algorithm. The new
estimator is shown to be consistent and retains root-n consistency under some
general conditions. Experiments based on simulated and real-world data show
that the proposed split-and-conquer approach has comparable statistical
performance with the global estimator based on the full dataset, if the latter
is feasible. It can even slightly outperform the global estimator if the model
assumption does not match the real-world data. It also has better statistical
and computational performance than some existing methods.
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