Share Email Print
cover

Proceedings Paper

Geometric multi-resolution analysis based classification for high dimensional data
Author(s): Dung N. Tran; Sang Peter Chin
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Data sets are often modeled as point clouds lying in a high dimensional space. In practice, they usually reside on or near a much lower dimensional manifold embedded in the ambient space; this feature allows for both a simple representation of the data as well as accurate performance for statistical inference procedures such as estimation, regression and classification. In this paper we propose a framework based on geometric multi-resolution analysis (GMRA) to tackle the problem of classifying data lying around a low-dimensional set M embedded in a high-dimensional space RD. We test our algorithms on real data sets and demonstrate its efficacy in the presence of noise.

Paper Details

Date Published: 18 June 2014
PDF: 8 pages
Proc. SPIE 9097, Cyber Sensing 2014, 90970L (18 June 2014); doi: 10.1117/12.2063316
Show Author Affiliations
Dung N. Tran, Johns Hopkins Univ. (United States)
Sang Peter Chin, Johns Hopkins Univ. (United States)
Draper Lab. (United States)


Published in SPIE Proceedings Vol. 9097:
Cyber Sensing 2014
Igor V. Ternovskiy; Peter Chin, Editor(s)

© SPIE. Terms of Use
Back to Top