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This paper presents a locally decoupled network parameter learning with localpropagation. Three elements are taken into account: (i) sets of nonlineartransforms that describe the representations at all nodes, (ii) a localobjective at each node related to the corresponding local representation goal,and (iii) a local propagation model that relates the nonlinear error vectors ateach node with the goal error vectors from the directly connected nodes. Themodeling concepts (i), (ii) and (iii) offer several advantages, including (a) aunified 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 learningin infinitely long, multi-path and multi-goal networks. Numerical experimentsvalidate the potential of the learning principle. The preliminary results showadvantages in comparison to the state-of-the-art methods, w.r.t. the learningtime and the network size while having comparable recognition accuracy.

Network Learning with Local Propagation