Material Detail

A Fast Algorithm for Recovery of Jointly Sparse Vectors based on the Alternating Direction Methods

A Fast Algorithm for Recovery of Jointly Sparse Vectors based on the Alternating Direction Methods

This video was recorded at 14th International Conference on Artificial Intelligence and Statistics (AISTATS), Ft. Lauderdale 2011. The standard compressive sensing (CS) aims to recover sparse signal from single measurement vector which is known as SMV model. By contrast, recovery of sparse signals from multiple measurement vectors is called MMV model. In this paper, we consider the recovery of jointly sparse signals in the MMV model where multiple signal measurements are represented as a matrix and the sparsity of signal occurs in common locations. The sparse MMV model can be formulated as a matrix (2; 1)-norm minimization problem, which is much more difficult to solve than the l1-norm minimization in standard CS. In this paper, we propose a very fast algorithm, called MMV-ADM, to solve the... Show More
Rate

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.