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.

本文提出了一个基于局部传播和局部目标的网络参数学习方法，在任何可表示为图形形式的网络中具有统一的学习原则，可以理解和解释局部和全局学习动态，并在学习时间和网络规模上具有先进方法的优势，同时具有可等同的识别准确度