Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization

  title={Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization},
  author={Alekh Agarwal and Anima Anandkumar and Prateek Jain and Praneeth Netrapalli and Rashish Tandon},
  journal={SIAM Journal on Optimization},
We consider the problem of sparse coding, where each sample consists of a sparse linear combination of a set of dictionary atoms, and the task is to learn both the dictionary elements and the mixing coefficients. Alternating minimization is a popular heuristic for sparse coding, where the dictionary and the coefficients are estimated in alternate steps, keeping the other fixed. Typically, the coefficients are estimated via l1 minimization, keeping the dictionary fixed, and the dictionary is… CONTINUE READING
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