0.0001 and $\lambda$ to 0.01.

### Baselines

We also provide a GCN based model as the baseline, which empirically constructs a $k$-NN graph as the underlying graph. $k$ is set to 20 in the classification task based on the density of point clouds. We mainly compare our JLGCN model against the baseline GCN, and also compare against other state-of-the-art methods, including methods tailored for point cloud learning such as PointNet [@qi2017pointnet], PointNet++ [@qi2017pointnet++], PointGCN [@zhang2018graph], RGCNN [@te2018rgcnn], and DGCNN [@wang2019dgcnn].

### Results

Tab. \[tab:results\_modelnet\] presents results for ModelNet40 classification. Our method improves the baseline by about 2% with only 3MB of parameters added, demonstrating the learned graph is superior to the empirical $k$-NN graph. Also, our method achieves comparable results compared to state-of-the-art methods for point loud learning, while our model is simpler and contains much smaller amount of parameters. Note that, our method achieves the best performance among all spectral graph convolution based methods.

[p[0.3in]{}p[0.9in]{}p[0.4in]{}p[0.4in]{}p[0.4in]{}]{} **Category** & **Algorithm** & & &\

  -----------
  Pointwise
  MLP
  -----------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

&

  -------------------
  PointNet
  [@qi2017pointnet]
  -------------------

  : Results of point cloud classification on ModelNet40.[]{data-label="tab:results_modelnet"}

& 40 & 86.0 & 89.2\

  -----------
  Pointwise
  MLP
  -----------

  : Results of 