For a Random Forest, we can construct a N x N
(where N
is the number of data points) proximity matrix P
where P[i,j]
is how "close" the i-th
data point is from the j-th
data point. In Gilles Loupes' PhD dissertation, he shows an example of a very beautiful proximity visualization using the MNIST dataset:
My question is - how are these proximity plots made? Is there any intuitive difference between that and your traditional distance/similarity matrices? For example, if I have N = 500
data points, should I run the proximity matrix through some sort of dimensionality reduction technique like PCA / SVD / t-SNE so it is of form 500 x 2
, and the visualize it?