Adaptive Graph Convolutional Network for Unsupervised Generalizable Tabular Representation Learning

Abstract

A challenging open problem in deep learning is the representation of tabular data. Unlike the popular domains such as image and text understanding, where the deep convolutional network is fashionable in many applications, there is still no widely used neural architecture that can effectively explore informative structure from tabular data. In addition, existing antoencoder-based nonlinear representation learning approaches that employ reconstruction loss, are incompetent to preserve discriminative information. As a step toward bridging these gaps, we propose a novel adaptive graph convolutional network (AdaGCN) for unsupervised generalizable tabular representation learning in this article. To be specific, we hypothesize that the keys to boosting the efficiency and practicality of learned representations lie in three aspects, i.e., adaptivity, unsupervised, and generalization. As a result, the adaptive graph learning module is first designed to remove the predefined rules in conventional GCN models, which can explore more local patterns on arbitrary tabular data. Moreover, our AdaGCN directly minimizes the difference between distributions of original tabular data and learned embeddings for training without any label information. Last but not least, the parametric property of AdaGCN makes the unseen data to be handled offline, which extremely expends the scope of applications. We present extensive experiments showing that AdaGCN significantly and consistently outperforms several representation learning and clustering methods on several real-world tabular datasets.