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 R^D. We test our algorithms on real data sets and demonstrate its efficacy in the presence of noise.

Date Published: 18 June 2014
PDF: 8 pages
Proc. SPIE 9097, Cyber Sensing 2014, 90970L (18 June 2014); doi: 10.1117/12.2063316

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