Date of Award
8-2010
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Engineering
Department
Electrical Engineering
Major Professor
Chen, Huimin
Second Advisor
Li, X. Rong
Third Advisor
Jilkov, Vesselin P.
Abstract
Random projection method is one of the important tools for the dimensionality reduction of data which can be made efficient with strong error guarantees. In this thesis, we focus on linear transforms of high dimensional data to the low dimensional space satisfying the Johnson-Lindenstrauss lemma. In addition, we also prove some theoretical results relating to the projections that are of interest when applying them in practical applications. We show how the technique can be applied to synthetic data with probabilistic guarantee on the pairwise distance. The connection between dimensionality reduction and compressed sensing is also discussed.
Recommended Citation
Vamulapalli, Harika Rao, "On Dimensionality Reduction of Data" (2010). University of New Orleans Theses and Dissertations. 1211.
https://scholarworks.uno.edu/td/1211
Rights
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