Abstract: Space-time is a profound concept in physics. This concept was shown to be
useful for dimensionality reduction. We present basic definitions with interest-
ing counter-intuitions. We give theoretical propositions to show that space-time
is a more powerful representation than Euclidean space. We apply this concept
to manifold learning for preserving local information. Empirical results on non-
metric datasets show that more information can be preserved in space-time
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