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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2211.06829 |
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| _version_ | 1866929478337298432 |
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| author | Shrivastava, Harsh Chajewska, Urszula |
| author_facet | Shrivastava, Harsh Chajewska, Urszula |
| contents | Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives information about their direct dependence. In this survey, we list out different methods and study the advances in techniques developed to recover CI graphs. We cover traditional optimization methods as well as recently developed deep learning architectures along with their recommended implementations. To facilitate wider adoption, we include preliminaries that consolidate associated operations, for example techniques to obtain covariance matrix for mixed datatypes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_06829 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Methods for Recovering Conditional Independence Graphs: A Survey Shrivastava, Harsh Chajewska, Urszula Machine Learning Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives information about their direct dependence. In this survey, we list out different methods and study the advances in techniques developed to recover CI graphs. We cover traditional optimization methods as well as recently developed deep learning architectures along with their recommended implementations. To facilitate wider adoption, we include preliminaries that consolidate associated operations, for example techniques to obtain covariance matrix for mixed datatypes. |
| title | Methods for Recovering Conditional Independence Graphs: A Survey |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2211.06829 |