Data quality for the inverse Ising problem

Abstract

There are many methods proposed for inferring parameters of the Ising model from given data, that is a set of configurations generated according to the model itself. However little attention has been paid until now to the data, e.g. how the data is generated, whether the inference error using one set of data could be smaller than using another set of data, etc. In this paper we address the data quality problem in the kinetic inverse Ising problem. We quantify the quality of data using effective rank of the correlation matrix, and show that data gathered in a out of-equilibrium regime has a better quality than data gathered in equilibrium for coupling reconstruction. We also propose a matrix-perturbation based method for tuning the quality of given data and for removing bad-quality (i.e. redundant) configurations from data.

17 pages, 6 figures

References in corpus (8)

• Identification of direct residue contacts in protein-protein interaction by message passing
• Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models
• High-dimensional Ising model selection using ${\ell }_1$-regularized logistic regression
• The Ising Model for Neural Data: Model Quality and Approximate Methods for Extracting Functional Connectivity
• Mean Field Theory For Non-Equilibrium Network Reconstruction
• Mean-field theory for the inverse Ising problem at low temperatures
• Inference of the sparse kinetic Ising model using the decimation method
• Learning of couplings for random asymmetric kinetic Ising models revisited: random correlation matrices and learning curves