Applying a randomized nearest neighbors algorithm to dimensionality reduction

In this thesis, I implemented a randomized nearest neighbors algorithm in order to optimize an existing dimensionality reduction algorithm. In implementation I resolved details that were not considered in the design stage, and optimized the nearest neighbor system for use by the dimensionality reduction system. By using the new nearest neighbor system as a subroutine, the dimensionality reduction system runs in time O(n log n) with respect to the number of data points. This enables us to examine data sets that were prohibitively large before.

Department dc:contributor.department

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

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2003

Author dc:creator

• Jayaraman, Gautam, 1981-

Advisor dc:contributor.advisor

• Joshua B. Tenenbaum.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/29665

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/29665