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Separating Populations with Wide Data: A Spectral Analysis

  • Avrim Blum
  • Amin Coja-Oghlan
  • Alan Frieze
  • Shuheng Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4835)

Abstract

In this paper, we consider the problem of partitioning a small data sample drawn from a mixture of k product distributions. We are interested in the case that individual features are of low average quality γ, and we want to use as few of them as possible to correctly partition the sample. We analyze a spectral technique that is able to approximately optimize the total data size—the product of number of data points n and the number of features K—needed to correctly perform this partitioning as a function of 1/γ for K > n. Our goal is motivated by an application in clustering individuals according to their population of origin using markers, when the divergence between any two of the populations is small.

Keywords

Random Matrix Product Distribution Singular Vector Graph Partitioning Spectral Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Avrim Blum
    • 1
  • Amin Coja-Oghlan
    • 1
  • Alan Frieze
    • 1
  • Shuheng Zhou
    • 1
  1. 1.Carnegie Mellon University, Pittsburgh, PA 15213USA

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