Provable Guarantees for Compressive Dynamic Robust PCA

The problem we study here is a natural extension of the dynamic robust PCA problem, where the measurements are compressive measurement of the data matrix. The goal here is to track the sparse outliers with time and update-and estimate the ``low dimensional subspace’’. A common application where the above model makes sense is in Dynamic functional MRI.

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