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I have a collection of data points. Each point has 6 dimensions (x1, x2,...x6). I want to find a relation between two dimension (e.g. x1 vs x2). What I have been doing so far is look for points where the other dimensions (x3 to x6) are relatively constant, by defining a band. This way I would get several groups of data points where only the two dimensions of interest would change.

I was wondering if there is a better way of analyzing the relationship between these two dimensions. I looked at PCA, but I have a feeling that it does not help me much. If I reduce the problem to two dimensions the axes are basically meaningless.

Can you guys give me some directions to look at?

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You might take a look at canonical correlation analysis. It tries to find correlation between two sets of data. I guess you could adapt it to explore your data.

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  • $\begingroup$ Yes, I just edited it, thx. $\endgroup$ – Robin Feb 24 '17 at 9:12
  • $\begingroup$ @debzsud You corrected the wrong "two", I fixed it all, and added link to wikipedia article. (my edit is pending approval at this moment) $\endgroup$ – VividD May 25 '17 at 19:52

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