  This paper presents a locally decoupled network parameter learning with local
propagation. Three elements are taken into account: (i) sets of nonlinear
transforms that describe the representations at all nodes, (ii) a local
objective at each node related to the corresponding local representation goal,
and (iii) a local propagation model that relates the nonlinear error vectors at
each node with the goal error vectors from the directly connected nodes. The
modeling concepts (i), (ii) and (iii) offer several advantages, including (a) a
unified learning principle for any network that is represented as a graph, (b)
understanding and interpretation of the local and the global learning dynamics,
(c) decoupled and parallel parameter learning, (d) a possibility for learning
in infinitely long, multi-path and multi-goal networks. Numerical experiments
validate the potential of the learning principle. The preliminary results show
advantages in comparison to the state-of-the-art methods, w.r.t. the learning
time and the network size while having comparable recognition accuracy.
