A constant factor approximation algorithm for a class of classification problems
Anupam Gupta, Éva Tardos · 2000
In a traditional classification problem, we wish to assign labels from a set to each of objects so that the labeling is consistent with some observed data that includes pairwise relationships among the objects. Kleinberg and Tardos recently formulated a general classification problem of this type, the “metric labeling problem”, and gave an approximation algorithm for it. The algorithm is based on solving a linear programming relaxation of a natural integer program and then randomized rounding. In this paper we consider an important case of the metric labeling problem, in which the metric is the truncated linear metric. This is a natural non-uniform and robust metric, and it arises in a number of applications. We give a combinatorial 4-approximation algorithm for this metric. Our algorithm is a natural local search method, where the local steps are based on minimum cut computations in an appropriately constructed graph. Our method extends previous work by Boykov, Veksler and Zabih on more restricted classes of metrics.