Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data

Simona Cocco (1, 2) , Rémi Monasson (2, 3)

1 LPS - Laboratoire de Physique Statistique de l'ENS
2 Simons Center for Systems Biology
3 LPTENS - Laboratoire de Physique Théorique de l'ENS [École Normale Supérieure]

We introduce a procedure to infer the interactions among a set of binary variables, based on their sampled frequencies and pairwise correlations. The algorithm builds the clusters of variables contributing most to the entropy of the inferred Ising model, and rejects the small contributions due to the sampling noise. Our procedure successfully recovers benchmark Ising models even at criticality and in the low temperature phase, and is applied to neurobiological data.

Domaines: Mécanique statistique [cond-mat.stat-mech]; Bio-Informatique, Biologie Systémique [q-bio.QM]; Bio-informatique [q-bio.QM]; Machine Learning [stat.ML]

Simona Cocco, Rémi Monasson. Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data. 2011. ⟨hal-00566281⟩