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 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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Jin, Jiejun

Advisor dc:contributor.advisor

• Zheng, Lizhong

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139414

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139414