I have a few abstract and high dimensional point clouds in the form of distance matrices. I want to do unsupervised learning on this dataset.
The problem is, I am not using one distance matrix, but treating each distance matrix as an object, and trying to differentiate between those objects. On one distance matrix it's very simple: we can do k-mean, hierarchical clustering, etc.
An exemplary problem in 3d would be to differentiate a group of 3d point clouds of cats from dogs. In 2d would be to differentiate the pictures from cats and dogs. But what are the state-of-the-art methods to apply it on distance matrices?
Although that was a general question to which I'm looking for an answer, here are more details on how I obtained the distance matrices: I am actually looking into a bunch of weighted networks. The entries in the distance matrices are approximately indicating the distances from one node to another in those networks. I used Dijkstra algorithm for calculating the distances between nodes.