XSCALE ISOCLUSTER: Difference between revisions

XSCALE ISOCLUSTER (view source)

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 == Notes ==
-* For meaningful results, the number of known values (N*(N-1)/2 is the number of pairwise correlation coefficients) should be (preferrably much) higher than the number of unknowns (1+n*(N-1)). This means that one needs at least 5 datasets if dim=2, and at least 7 if dim=3.
+* For meaningful results, the number of known values [N*(N-1)/2 is the number of pairwise correlation coefficients] should be (preferrably much) higher than the number of unknowns (1+n*(N-1)). This means that one needs at least 5 data sets if dim=2, and at least 7 if dim=3.
-* The clustering of datasets in a low-dimensional space uses the method of Rodriguez and Laio (2014) ''Science'' '''344''', 1492-1496.
+* The clustering of data sets in a low-dimensional space uses the method of Rodriguez and Laio (2014) ''Science'' '''344''', 1492-1496.
+* The eigenvalues are printed out by the program, and can be used to deduce the proper value of the required dimension n. To make use of this, one should run with a high value of dim (e.g. 5), and inspect the list of eigenvalues with the goal of finding a significant drop in magnitude (e.g. a factor of 3 drop between the second and third eigenvalue would point to the third eigenvector being of low importance).
 * A different but related program is [[xds_nonisomorphism]].