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Detecting low-rank clusters via random sampling
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DOI：10.1016/j.jcp.2011.09.008.png)
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Abstract
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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. (C) 2011 Elsevier Inc. All rights reserved.
Keywords:
Random rotation projection
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