Fast recovery from a union of subspaces

NeurIPS 2016 Chinmay HegdePiotr IndykLudwig Schmidt

We address the problem of recovering a high-dimensional but structured vector from linear observations in a general setting where the vector can come from an arbitrary union of subspaces. This setup includes well-studied problems such as compressive sensing and low-rank matrix recovery... (read more)

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