Maximal Correlation Feature Selection and Suppression With Applications
In standard supervised learning, we assume that we are trying to learn some target variable 𝑌 from some data 𝑋. However, many learning problems can be framed as supervised learning with an auxiliary objective, often associated with an auxiliary variable 𝐷 which defines this objective. Applying the p...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/140035 |