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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.11163 |
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| _version_ | 1866914767665364992 |
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| author | Li, Xuetong Gao, Yuan Chang, Hong Huang, Danyang Ma, Yingying Pan, Rui Qi, Haobo Wang, Feifei Wu, Shuyuan Xu, Ke Zhou, Jing Zhu, Xuening Zhu, Yingqiu Wang, Hansheng |
| author_facet | Li, Xuetong Gao, Yuan Chang, Hong Huang, Danyang Ma, Yingying Pan, Rui Qi, Haobo Wang, Feifei Wu, Shuyuan Xu, Ke Zhou, Jing Zhu, Xuening Zhu, Yingqiu Wang, Hansheng |
| contents | This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades. In this work, we focus on three categories of statistical computation methods: (1) distributed computing, (2) subsampling methods, and (3) minibatch gradient techniques. The first class of literature is about distributed computing and focuses on the situation, where the dataset size is too huge to be comfortably handled by one single computer. In this case, a distributed computation system with multiple computers has to be utilized. The second class of literature is about subsampling methods and concerns about the situation, where the sample size of dataset is small enough to be placed on one single computer but too large to be easily processed by its memory as a whole. The last class of literature studies those minibatch gradient related optimization techniques, which have been extensively used for optimizing various deep learning models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11163 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques Li, Xuetong Gao, Yuan Chang, Hong Huang, Danyang Ma, Yingying Pan, Rui Qi, Haobo Wang, Feifei Wu, Shuyuan Xu, Ke Zhou, Jing Zhu, Xuening Zhu, Yingqiu Wang, Hansheng Methodology Machine Learning Statistics Theory Computation This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades. In this work, we focus on three categories of statistical computation methods: (1) distributed computing, (2) subsampling methods, and (3) minibatch gradient techniques. The first class of literature is about distributed computing and focuses on the situation, where the dataset size is too huge to be comfortably handled by one single computer. In this case, a distributed computation system with multiple computers has to be utilized. The second class of literature is about subsampling methods and concerns about the situation, where the sample size of dataset is small enough to be placed on one single computer but too large to be easily processed by its memory as a whole. The last class of literature studies those minibatch gradient related optimization techniques, which have been extensively used for optimizing various deep learning models. |
| title | A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques |
| topic | Methodology Machine Learning Statistics Theory Computation |
| url | https://arxiv.org/abs/2403.11163 |