- Series
- Stochastics Seminar
- Time
- Tuesday, October 9, 2012 - 3:05pm for 1 hour (actually 50 minutes)
- Location
- Skyles 005
- Speaker
- Yiyuan She – Florida State University
- Organizer
- Karim Lounici
Rank reduction as an effective technique for dimension reduction is
widely used in statistical modeling and machine learning. Modern
statistical applications entail high dimensional data analysis where
there may exist a large number of nuisance variables. But the plain rank
reduction cannot discern relevant or important variables. The talk
discusses joint variable and rank selection for predictive learning. We
propose to apply sparsity and reduced rank techniques to attain
simultaneous feature selection and feature extraction in a vector
regression setup. A class of estimators is introduced based on novel
penalties that impose both row and rank restrictions on the coefficient
matrix. Selectable principle component analysis is proposed and studied
from a self-regression standpoint which gives an extension to the sparse
principle component analysis. We show that these estimators adapt to the
unknown matrix sparsity and have fast rates of convergence in comparison
with LASSO and reduced rank regression. Efficient computational
algorithms are developed and applied to real world applications.