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Implementing regularization implicitly via approximate eigenvector computation
[article]

2011
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arXiv
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pre-print

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 optimizing the modified objective function. This procedure often leads to optimization problems that are computationally more expensive than the original problem, a fact that is clearly problematic if one is interested in large-scale applications. On the other hand, a large body of empirical work

arXiv:1010.0703v2
fatcat:bzaxcrxqjncvhmienazxrdnola