|
Abstract |
This short course will focus on compressed sensing and high dimensional linear regression. These and other related problems have attracted much recent interest in a range of fields including statistics, machine learning and electrical engineering. In the high‐dimensional setting where the dimension p can be much larger than the sample size n, classical methods and results based on fixed p and large n are no longer applicable.
1．Constrained and penalized l_1 minimization methods for compressed sensing
2．High‐dimensional regression
3．Give a unified and elementary analysis on sparse signal recovery in three settings: noiseless, bounded noise and Gaussian noise
4． Construction of compressed sensing matrices will also be discussed |
