  In this paper, we propose a novel graph-based approach for semi-supervised
learning problems, which considers an adaptive adjacency of the examples
throughout the unsupervised portion of the training. Adjacency of the examples
is inferred using the predictions of a neural network model which is first
initialized by a supervised pretraining. These predictions are then updated
according to a novel unsupervised objective which regularizes another
adjacency, now linking the output nodes. Regularizing the adjacency of the
output nodes, inferred from the predictions of the network, creates an easier
optimization problem and ultimately provides that the predictions of the
network turn into the optimal embedding. Ultimately, the proposed framework
provides an effective and scalable graph-based solution which is natural to the
operational mechanism of deep neural networks. Our results show comparable
performance with state-of-the-art generative approaches for semi-supervised
learning on an easier-to-train, low-cost framework.
