baseline significantly, and keeps high accuracy even when the point cloud density is quite low.

![Robustness test with different missing ratios of point clouds.[]{data-label="fig:robust_pc"}](figures/robust_pc.pdf){width="3in"}

Conclusion {#sec:conclude}
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We propose joint learning of graphs and node features in Graph Convolutional Neural Networks, which dynamically learns graphs adaptive to the structure of node features in different layers. In particular, we optimize an underlying graph kernel via distance metric learning with the Mahalanobis distance employed. The metric matrix is decomposed into a low-dimensional matrix and optimized based on Graph Laplacian Regularizer. Extensive experiments demonstrate the superiority and robustness of our method in semi-supervised learning and point cloud learning problems.

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Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou 