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Efficiently Using Second Order Information in Large l1 Regularization Problems
Efficiently Using Second Order Information Large l1 Regularization Problems
2013/4/28
We propose a novel general algorithm LHAC that efficiently uses second-order information to train a class of large-scale l1-regularized problems. Our method executes cheap iterations while achieving f...
Convex Tensor Decomposition via Structured Schatten Norm Regularization
Convex Tensor Decomposition Structured Schatten Norm Regularization
2013/4/28
We discuss structured Schatten norms for tensor decomposition that includes two recently proposed norms ("overlapped" and "latent") for convex-optimization-based tensor decomposition, and connect tens...
Implementing regularization implicitly via approximate eigenvector computation
Implementing regularization implicitly via approximate eigenvector computation
2010/10/19
Regularization is a powerful technique for extracting useful information from noisy data. Typically, it is implemented by adding some sort of norm constraint to an objective function and then exactly...