Graph structured autoencoder.

Abstract

In this work, we introduce the graph regularized autoencoder. We propose three variants. The first one is the unsupervised version. The second one is tailored for clustering, by incorporating subspace clustering terms into the autoencoder formulation. The third is a supervised label consistent autoencoder suitable for single label and multi-label classification problems. Each of these has been compared with the state-of-the-art on benchmark datasets. The problems addressed here are image denoising, clustering and classification. Our proposed methods excel of the existing techniques in all of the problems.