I have 6,000 points for which I have all pairwise distances in a distance matrix. I want to get an idea whether these data were generated by a mixture of Gaussian distributions so I'm trying to get a visualization. I am trying to apply multidimensional scaling in 2 dimensions using sklearn in Python.
I have four questions:
- In all the examples I found, the number of points were very limited (around 20). Is it possible to apply MDS with 6,000 points?
- If not, is there any way I can get a better idea whether my data were generated by a mixture of Gaussians? (I want to cluster this data using GMM.)
- sklearn uses the function manifold.MDS. One of the parameters of this function is 'random state'. What does it represent?
- In this example, the matrix used (X_true) has dimension 20x2. Shouldn't MDS take a distance matrix (i.e., a square matrix)?