Nonlinear Structure Detection in High Dimensional Data Analysis
September 29, 2010
3:00pm SEC 204
Abstract
High dimensional data become ubiquitous in modern science and engineering.
Some data are known or considered to have nonlinear structures and linear
methods usually do not work well. We developed a series of methods that can
detect nonlinear structures or nonlinear dependence underlying the data.
These methods involve the use of reproducing kernel Hilbert spaces
and l1
minimization technique. The reproducing kernel Hilbert space enables
nonlinear solutions to describe nonlinear structures by tuning the kernels
and is computationally efficient due to the reproducing property.
The l1
minimization allows sparsity to eliminate irrelevant information. The
utility of our methods is validated in various simulations and applications
such as gene expression data analysis, hand written digits recognition,
biomedical time series, and stylometry analysis.
Webmaster University of Houston
---
Last modified: April 11 2016 - 18:14:43