  Many modern data-intensive computational problems either require, or benefit
from distance or similarity data that adhere to a metric. The algorithms run
faster or have better performance guarantees. Unfortunately, in real
applications, the data are messy and values are noisy. The distances between
the data points are far from satisfying a metric. Indeed, there are a number of
different algorithms for finding the closest set of distances to the given ones
that also satisfy a metric (sometimes with the extra condition of being
Euclidean). These algorithms can have unintended consequences, they can change
a large number of the original data points, and alter many other features of
the data.
  The goal of sparse metric repair is to make as few changes as possible to the
original data set or underlying distances so as to ensure the resulting
distances satisfy the properties of a metric. In other words, we seek to
minimize the sparsity (or the $\ell_0$ "norm") of the changes we make to the
distances subject to the new distances satisfying a metric. We give three
different combinatorial algorithms to repair a metric sparsely. In one setting
the algorithm is guaranteed to return the sparsest solution and in the other
settings, the algorithms repair the metric. Without prior information, the
algorithms run in time proportional to the cube of the number of input data
points and, with prior information we can reduce the running time considerably.
