Abstract:Low‐rank matrices play a central role in modelling and computational methods for signal processing and large‐scale data analysis. Real‐world observed data are often sampled from low‐dimensional subspaces, but with sample‐specific corruptions (i.e. outliers) or random noises. In many applications where low‐rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. The authors aim to re… Show more
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