On Dimensionality Reduction of Data

<p>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.</p>

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical Engineering

Year

2010

Author dc:creator

• Vamulapalli, Harika Rao

Contributors dc:contributor

• Chen, Huimin
• Li, X. Rong
• Jilkov, Vesselin P.

• principal component analysis
• dimensionality reduction
• random projection
• fast Johnson Lindenstrauss transform

Repository record dc:identifier

https://scholarworks.uno.edu/td/1211

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-2194