An Information-centric Algorithm for Feature Extraction in High-dimensional Data
Name
Jin-jjjin-SM-EECS-2021-thesis.pdf
Description
Thesis PDF
Size
420.19 KB
Format
Adobe PDF
Checksum (MD5)
1cb1a03915c29db1fabe791951a7b1c3
Author(s)
Jin, Jiejun
Advisor(s)
Zheng, Lizhong
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
