An optimization parameter for seriation of noisy data

Abstract

A square symmetric matrix is a Robinson similarity matrix if entries in its rows and columns are non-decreasing when moving towards the diagonal. A Robinson similarity matrix can be viewed as the affinity matrix between objects arranged in linear order, where objects closer together have higher affinity. We define a new parameter, $\Gamma_\max$, which measures how badly a given matrix fails to be Robinson similarity. Namely, a matrix is Robinson similarity precisely when its $\Gamma_\max$ attains zero, and a matrix with small $\Gamma_\max$ is close (in the normalized $\ell^1$-norm) to a Robinson similarity matrix. Moreover, both $\Gamma_\max$ and the Robinson similarity approximation can be computed in polynomial time. Thus, our parameter recognizes Robinson similarity matrices which are perturbed by noise, and can therefore be a useful tool in the problem of seriation of noisy data.