  In this paper, we propose a multi-step inertial Forward--Backward splitting
algorithm for minimizing the sum of two non-necessarily convex functions, one
of which is proper lower semi-continuous while the other is differentiable with
a Lipschitz continuous gradient. We first prove global convergence of the
scheme with the help of the Kurdyka-{\L}ojasiewicz property. Then, when the
non-smooth part is also partly smooth relative to a smooth submanifold, we
establish finite identification of the latter and provide sharp local linear
convergence analysis. The proposed method is illustrated on a few problems
arising from statistics and machine learning.
