Supervised subspace learning based on deep randomized networks

In this paper, we propose a supervised subspace learning method that exploits the rich representation power of deep feedforward networks. In order to derive a fast, yet efficient, learning scheme we employ deep randomized neural networks that have been recently shown to provide good compromise between training speed and performance. For optimally determining the learnt subspace, we formulate a regression problem where we employ target vectors designed to encode both the labeling information available for the training data and geometric properties of the training data, when represented in the feature space determined by the network's last hidden layer outputs. We experimentally show that the proposed approach is able to outperform deep randomized neural networks trained by using the standard network target vectors.