A Graph Regularized Deep Neural Network for Unsupervised Image Representation Learning

Abstract

Deep Auto-Encoder (DAE) has shown its promising power in high-level representation learning. From the perspective of manifold learning, we propose a graph regularized deep neural network (GR-DNN) to endue traditional DAEs with the ability of retaining local geometric structure. A deep-structured regularizer is formulated upon multi-layer perceptions to… (More)
DOI: 10.1109/CVPR.2017.746

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