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# Guangliang Chen : Modeling High Dimensional Data by Linear/Nonlinear Models

Nowadays researchers encounter high dimensional data that arises in a variety of forms such as digital images, videos, and hyperspectral images. How to efficiently and effectively modeling such data sets has become an active research topic. A common model is to approximate such data by a mixture of affine subspaces. In this talk I will present a fast and accurate algorithm that can solve this problem in full generality, addressing both theoretical and applied issues. If time permits, I will also talk about the use of nonlinear models. This is joint work with Mauro Maggioni.

**Category**: Graduate/Faculty Seminar**Duration**: 01:34:51**Date**: December 2, 2011 at 4:25 PM**Views**: 117-
**Tags:**seminar, Graduate/faculty Seminar

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