Robust estimation of highly corrupted low‐rank matrix via alternating direction method of multiplier
Robust estimation of highly corrupted low‐rank matrix via alternating direction method of multiplier
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 …