Best way for correlation analysis I am looking for an alternative way of analysing my data which will be used for ML. I am using Matlab to implement my code. Using my code, I obtain arrays which are used to describe signals. Each array represents a new and different signal.

I have used the corrcoeff method in Matlab and it delivered what I needed. But, I need another alternative way to verify the correlation between all the signals. Is there a better way to do this which is easy to interpret? 

Dumb question: is it better to compare the images of the signal generated by Matlab or to compare the arrays?

Forgive me if this sounds badly formulated but I hope it is clear.

Thank you in advanced!
Based on the current details about your question, I'm not sure what you seek, but I will give it a try.

I assumed you already tried Pearson's coefficient. There are other metrics, which are more or less difficult to implement if not already existing in matlab.


Spearman's rank
Coefficient of determination
Hilbert-Schmidt independence criterion


All those will provide some insight about dependencies within your dataset.