This session introduces the basic concepts of sparse representation and low-rank representation. The emphasis will be on how to model and recover low-dimensional structures in high-dimensional signals, and how to verify that the models are appropriate. We will illustrate this process through examples drawn from a number of vision applications. We will gently introduce the foundational theoretical results in this area, and show how theory informs the modeling process. Joint talk by Yi Ma and John Wright
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