Recursive ridge regression using second-order stochastic algorithms

Abstract

Recursive second-order stochastic algorithms are presented for solving ridge regression problems in the linear and binary logistic case. The proposed algorithms allow to update the estimates of ridge solution when the data arrive in continuous flow. Some guarantees on the almost sure behavior of the proposed algorithms are established. Numerical experiments on simulated and real-world data show the advantages of our algorithms compared to alternative methods.