Abstract
We present an algorithm for detecting a low-rank cluster of vectors from within a much larger group of vectors. This algorithm relies on a basic geometric property of high-dimensional space: Most of the volume of a typical eccentric ellipsoid is confined to relatively few orthants within the ambient space. This simple fact can be used to quickly detect a collection of vectors with low numerical rank from amongst a larger group of vectors with higher numerical rank.
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