Dimension Reduction with Random Projections
12:00:00 - 13:15:00
308 , Mathematics Research Center Building (ori. New Math. Bldg.)
Random projection is a promising dimension reduction technique for high-dimensional data analysis. Johnson-Lindenstrauss Lemma states that a set of points in a high-dimensional space can be embedded into a space of lower dimension in such a way that distances between the points are nearly preserved. Mixtures of Gaussians are among the most fundamental and widely used statistical models. In this presentation, we will show that, under some conditions, means of separated Gaussians can still be kept separated through random projections. Some numerical experiments with Gaussian mixtures will be illustrated.