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dc.contributor.advisorZheng, Lizhong
dc.contributor.authorJin, Jiejun
dc.date.accessioned2022-01-14T15:10:05Z
dc.date.available2022-01-14T15:10:05Z
dc.date.issued2021-06
dc.date.submitted2021-06-24T19:22:59.545Z
dc.identifier.urihttps://hdl.handle.net/1721.1/139414
dc.description.abstractThis 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 hidden Markov model is used to consider the relation between high-dimensional data and their low-dimensional features. Feature extraction is regarded as an optimization problem to figure out the corresponding information vector space. Several approaches are proposed to solve this problem and mathematical proof is provided to validate the effectiveness of them.
dc.publisherMassachusetts Institute of Technology
dc.rightsIn Copyright - Educational Use Permitted
dc.rightsCopyright MIT
dc.rights.urihttp://rightsstatements.org/page/InC-EDU/1.0/
dc.titleAn Information-centric Algorithm for Feature Extraction in High-dimensional Data
dc.typeThesis
dc.description.degreeS.M.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
mit.thesis.degreeMaster
thesis.degree.nameMaster of Science in Electrical Engineering and Computer Science


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