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After the tremendous development of neural networks trained bybackpropagation, it is a good time to develop other algorithms for trainingneural networks to gain more insights into networks. In this paper, we proposea new algorithm for training feedforward neural networks which is fairly fasterthan backpropagation. This method is based on projection and reconstructionwhere, at every layer, the projected data and reconstructed labels are forcedto be similar and the weights are tuned accordingly layer by layer. Theproposed algorithm can be used for both input and feature spaces, named asbackprojection and kernel backprojection, respectively. This algorithm gives aninsight to networks with a projection-based perspective. The experiments onsynthetic datasets show the effectiveness of the proposed method.

Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces