JSM 2005 Online Program

Abstract #303868

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	354
Type:	Topic Contributed
Date/Time:	Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Nonparametric Statistics
Abstract - #303868
Title:	A Scale-based Approach to Finding Effective Dimensionality
Author(s):	Xiaohui Wang*+ and James Marron
Companies:	University of Virginia and University of North Carolina, Chapel Hill
Address:	Kerchof Hall, Charlottesville, VA, 22901, United States
Keywords:	manifold learning ; scale space ; nonparametric statistic
Abstract:	Low-dimensional manifolds are an important type of structure in high-dimensional datasets. Our proposed work is to identify low-dimensional manifolds that contain (perhaps module some "noise") the data. The dimension of such a manifold is called the "effective dimensionality" of the data. Unlike classical techniques, such as principal component analysis and multidimensional scaling, our approach is capable of discovering interesting nonlinear structure in the data. The scale space viewpoint is important to our approach to meet the challenge of noisy data. Our approach finds the "effective dimensionality" of the data over all scale without any prior knowledge, which gives substantial improvement over earlier methods.

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