Stacked Robust Autoencoder for Classification

In this work we propose an l(p)-norm data fidelity constraint for training the autoencoder. Usually the Euclidean distance is used for this purpose; we generalize the l(2)-norm to the l(p)-norm; smaller values of p make the problem robust to outliers. The ensuing optimization problem is solved using the Augmented Lagrangian approach. The proposed l(p)-norm Autoencoder has been tested on benchmark deep learning datasets - MNIST, CIFAR-10 and SVHN. We have seen that the proposed robust autoencoder yields better results than the standard autoencoder (l(2)-norm) and deep belief network for all of these problems.