An Information-centric Algorithm for Feature Extraction in High-dimensional Data
This thesis develops a novel technique for extracting features in high-dimensional data. The proposed method is based on the concept of maximal correlation and local information theory, which demonstrates the importance of the information vector space in feature extraction. More specifically, a hidd...
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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/1721.1/139414 |