End-To-End Graph-based Deep Semi-Supervised Learning

• URL: http://arxiv.org/abs/2002.09891v1
• Date: Sun, 23 Feb 2020 12:32:08 GMT
• Title: End-To-End Graph-based Deep Semi-Supervised Learning
• Authors: Zihao Wang, Enmei Tu, Zhou Meng
• Abstract summary: The quality of a graph is determined jointly by three key factors of the graph nodes, edges and similarity measure (or edge weights) We propose a novel graph-based semi-supervised learning approach to optimize all three factors simultaneously in an end-to-end learning fashion.
• Score: 7.151859287072378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quality of a graph is determined jointly by three key factors of the graph: nodes, edges and similarity measure (or edge weights), and is very crucial to the success of graph-based semi-supervised learning (SSL) approaches. Recently, dynamic graph, which means part/all its factors are dynamically updated during the training process, has demonstrated to be promising for graph-based semi-supervised learning. However, existing approaches only update part of the three factors and keep the rest manually specified during the learning stage. In this paper, we propose a novel graph-based semi-supervised learning approach to optimize all three factors simultaneously in an end-to-end learning fashion. To this end, we concatenate two neural networks (feature network and similarity network) together to learn the categorical label and semantic similarity, respectively, and train the networks to minimize a unified SSL objective function. We also introduce an extended graph Laplacian regularization term to increase training efficiency. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach.

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