Inference of principal components of noisy correlation matrices with prior information

Rémi Monasson (1)

1 LPTENS - Laboratoire de Physique Théorique de l'ENS [École Normale Supérieure]

The problem of infering the top component of a noisy sample covariance matrix with prior information about the distribution of its entries is considered, in the framework of the spiked covariance model. Using the replica method of statistical physics the computation of the overlap between the top components of the sample and population covariance matrices is formulated as an explicit optimization problem for any kind of entry-wise prior information. The approach is illustrated on the case of top components including large entries, and the corresponding phase diagram is shown. The calculation predicts that the maximal sampling noise level at which the recovery of the top population component remains possible is higher than its counterpart in the spiked covariance model with no prior information.

Mots clés: Random Matrix Theory; Spiked Covariance Model; Prior Information; Replica Method; Phase Transitions

Domaines: Mécanique statistique [cond-mat.stat-mech]

Rémi Monasson. Inference of principal components of noisy correlation matrices with prior information. Asilomar Conference on Signals, Systems, and Computers, Nov 2016, Monterey, United States. ⟨hal-01419083⟩