Component Pursuit from Noisy Measurements

Abstract

To achieve efficient component pursuit from noisy measurements, a learning algorithm is presented that combines standard gradient principle and the standard stochastic approximations. By extending the linear predictor principle from noise-free case, a proper objective function is introduced which has the same generic form as that for the noise-free case. Extensive computer simulations are performed to illustrate the power of the presented technique.