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Premise: I have a dataset of elements for which I have a representation in 2 different spaces, a "latent" space and the original space (I can move between those with an Autoencoder neural network).

I want to prove a conjecture where I think that a distance that I developed for the original space doesn't "correlate" with the euclidean distance in the latent space. (By correlate I mean that I feel that "close" elements in one space are "distant" in the other, by their respective distances)

I have computed the distance matrix for both the distances, each in its relative space, for each pair of samples in the dataset.

How do you suggest I can prove this or verify it?

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I would use the Pearson correlation coefficient. After doing the test you can look at the p-value to quantify the strength of the correlation.

If you use Python you can use scipy.stats.pearsonr(x, y) to do so, where is x is the distance matrix for one of the spaces and y the other.

Have a look at the documentation here: https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.pearsonr.html

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  • $\begingroup$ Thank you, I will try it right away. Anyway, could you provide at least an insight of what this Pearson correlation coefficient is doing? The documentation is not super clear on this. I will let you know how the result looks like. $\endgroup$ – Luca Angioloni Oct 8 at 13:50
  • $\begingroup$ So, first of all the function suggested accepts 1-D inputs, and not the distance matrices. Then even if I do it row wise, I do not know how to interpret the results... For instance this is the output of the first row compared with the first row: (0.009394309417862736, 0.77945516455912) $\endgroup$ – Luca Angioloni Oct 8 at 13:58
  • $\begingroup$ Ok, i didnt recognize that. You can use numpy.ndarray.flatten on both matrices respectively in order to make them 1d. $\endgroup$ – tamtam_ Oct 8 at 14:22
  • $\begingroup$ The first output value is the Pearson coefficient [-1, 1]. If it is close to zero, which it is, it indicates no correlation. The p-value also indicates the same. If the p-value is below 0.05, it would indicate a linear correlation, which is not the case here. $\endgroup$ – tamtam_ Oct 8 at 14:27
  • $\begingroup$ Ok, thanks. So this is what I get now: pearsonr(D.flatten(), D_Z.flatten()) => (0.011961790971000504, 1.5945123128090787e-26) $\endgroup$ – Luca Angioloni Oct 8 at 14:32

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