Material Detail

Learning Sparsely Used Overcomplete Dictionaries

Learning Sparsely Used Overcomplete Dictionaries

This video was recorded at 27th Annual Conference on Learning Theory (COLT), Barcelona 2014. We consider the problem of learning sparsely used overcomplete dictionaries, where each observation is a sparse combination of elements from an unknown overcomplete dictionary. We establish exact recovery when the dictionary elements are mutually incoherent. Our method consists of a clustering-based initialization step, which provides an approximate estimate of the true dictionary with guaranteed accuracy. This estimate is then refined via an iterative algorithm with the following alternating steps: 1) estimation of the dictionary coefficients for each observation through ℓ1 minimization, given the dictionary estimate, and 2) estimation of the dictionary elements through least squares, given 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.
hidden