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I am recreating a project that uses multidimensional scaling (MDS) to visualise the data in the final stage. Specifically, the original work uses MATLAB's mdscale with the metricsstress parameter which according to the documentation uses 'Squared stress, normalized with the sum of 4th powers of the dissimilarities'.

My preferred environment is python and the only implementation of MDS I'm aware of is sklearn.manifold.MDS which uses SMACOF. Here stress as the 'sum of squared distance of the disparities and the distances for all constrained points' but nothing is said about the normalisation.

My question is: were I to use the sklearn implementation in place of the mdscale one, would the results be comparable?

lovelyzoo
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  • Why dont you just...... execute both... and see – Ander Biguri Jul 10 '15 at 09:00
  • Specifically, my professional situation is such that the sklearn implementation is more readily available to me. As such, I'm just... trying to draw on the experience of my peers... to see if I can save myself a lot of unnecessary effort. – lovelyzoo Jul 30 '15 at 16:30

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