OPTIMIZATION AND DATA-DRIVEN METHODS FOR SIGNAL PROCESSING
By exploiting and leveraging the intrinsic properties of the observed signal, many signal processing and machine learning problems can be effectively solved by transforming them into optimization problems, which constitutes the first part of the thesis. The theoretical sample complexity for exact signal recovery and the recovery error bound with noisy observation can be derived for the optimization methods. However, it is not efficient for optimization methods to deal with high-dimensional signals and observation with the complex noise and non-stationary sensing process. Thus, in the second part ...
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