Post #151

Learning graph structure to help classification I just recently discussed an idea whether it's possible to create a graph from a non-graph classification data set and improve classification performance by doing it and I found two works on it. First approach just tries different values for knn to connect the points into a graph, obtains a graph for each parameter setting, and verifies the performance of classification of a graph model on the obtained graph. Clearly the problem with it is that you have to do classification many many times for different parameters of your knn. Second approach (ICML 2019, link to presentation) is more data-driven: instead of freezing the parameters of knn, it uses a graph generative model that would generate a graph from the points. Then a graph neural network would make a classification prediction and, together with parameters of generative model, would be updated by backpropagation. It's still quite heavy as you need to update parameters of two different models instead of one. Perhaps, there are future works that would create the graphs at little computational cost and would boost the results for classification pipelines.