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Select samples on the exterior of a data space based on a Euclidean distance.


isel = distslct(x,nosamps,flag)


DISTSLCT first identifies a sample in the M by N data set x furthest from the data set mean. Subsequent samples are selected to be simultaneously the furthest from the mean and the selected samples for a total of nosamps selected samples. DISTSLCT calls STDSSLCT to find the number of samples up to the rank of the data and uses a distance measure to find additional samples if nosamps>rank(x).

Optional intput tells DISTSLCT how many samples STDSLCT should estimate when nosamps>N:

  • 1 = STDSLCT selects N-1, or
  • 2 = STDSLCT selects N {default}.

Output isel is a vector of length nosamps containing the indices of the selected samples.

This routine is used to initialize the selection of samples in the DOPTIMAL function. Altough it does not satisfy the d-optimality condition, it is an alternative to doptimal that does not require an inverse or calculation of a determinant.


  • x: data set, M by N
  • nosamps: number of selected samples

Optional Inputs

  • flag: how many samples to select when nosamps>N; a value of 1 selects N-1, while a value of 2 (default) selects N.


  • isel: vector containing the indices of the selected samples

See Also

doptimal, reducennsamples, stdsslct, splitcaltest