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🔗 Source: learningtheory.org
📊 Size: 234 KB
📄 Pages: 13 pages
⬇️ Downloads: 46

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It considers a penalized empirical risk minimization problem with a convex loss function and a negative entropy penalty. The goal is to extend previous results on sparse recovery over linear spans or convex hulls of large finite dictionaries to the case of infinite dictionaries. The authors prove that the "approximate sparsity" of the true penalized solution implies the "approximate sparsity" of the empirical solution with high probability, and derive probabilistic bounds on the random error and the Kullback-Leibler type distance between the empirical and true solutions.

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Description

It considers a penalized empirical risk minimization problem with a convex loss function and a negative entropy penalty.

Technical Information

  • File Format: PDF
  • File Size: 234 KB
  • Pages: 13
  • Language: EN
  • Total Downloads: 46
  • Last Updated: 6 hours ago

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