Title :
Simple algorithms for sparse linear regression with uncertain covariates
Author :
Chen, Yudong ; Caramanis, Constantine
Author_Institution :
Univ. of Texas at Austin, Austin, TX, USA
Abstract :
In this short paper we consider sparse linear regression with missing or noisy covariates. This problem has recently attracted some attention, with the best known results given in [1], where they use a projected gradient approach to approximately solve a non convex optimization problem. Here we show that an extremely simple, lower complexity algorithm that achieves the same (or better) bounds for support recovery.
Keywords :
compressed sensing; concave programming; gradient methods; regression analysis; signal reconstruction; complexity algorithm; compressed sensing; nonconvex optimization problem; projected gradient approach; sparse linear regression analysis; support recovery; uncertain covariation; Algorithm design and analysis; Educational institutions; Linear regression; Noise measurement; Random variables; Signal processing algorithms; Vectors;
Conference_Titel :
Statistical Signal Processing Workshop (SSP), 2012 IEEE
Conference_Location :
Ann Arbor, MI
Print_ISBN :
978-1-4673-0182-4
Electronic_ISBN :
pending
DOI :
10.1109/SSP.2012.6319718