Autoencoder-based graph construction for semi-supervised learning

Abstract

We consider graph-based semi-supervised learning that leverages a similarity graph across data points to better exploit data structure exposed in unlabeled data. One challenge that arises in this problem context is that conventional matrix completion which can serve to construct a similarity graph entails heavy computational overhead, since it re-trains the graph independently whenever model parameters of an interested classifier are updated. In this paper, we propose a holistic approach that employs a parameterized neural-net-based autoencoder for matrix completion, thereby enabling simultaneous training between models of the classifier and matrix completion. We find that this approach not only speeds up training time (around a three-fold improvement over a prior approach), but also offers a higher prediction accuracy via a more accurate graph estimate. We demonstrate that our algorithm obtains stateof-the-art performances by respectful margins on benchmark datasets: Achieving the error rates of 0.57% on MNIST with 100 labels; 3.48% on SVHN with 1000 labels; and 6.87% on CIFAR-10 with 4000 labels.