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Chapter introduces the Huber norm
Huber (1973). The Huber norm is an hybrid error
measure that is robust to outliers. Because it is differentiable everywhere,
the Huber norm can be minimized with a gradient-based algorithm.
In Chapter , I propose minimizing the Huber norm
with a quasi-Newton method called L-BFGS. This technique can solve any non-linear
problem where local-minima are sought. Its limited-memory requirements make it
also very attractive for solving large-scale problems.
On a velocity estimation problem, this Chapter illustrates
that the Huber norm with the L-BFGS solver is robust to outliers, thus
providing an alternative to the norm.

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Stanford Exploration Project

5/5/2005