FINDING LARGE AVERAGE SUBMATRICES IN HIGH DIMENSIONAL DATA

@inproceedings{Shabalin2009FINDINGLA,
  title={FINDING LARGE AVERAGE SUBMATRICES IN HIGH DIMENSIONAL DATA},
  author={Andrey A. Shabalin and Victor J. Weigman and Charles M. Perou and Andrew B. Nobel},
  year={2009}
}
  • Andrey A. Shabalin, Victor J. Weigman, +1 author Andrew B. Nobel
  • Published 2009
  • Mathematics, Biology
  • The search for sample-variable associations is an important problem in the exploratory analysis of high dimensional data. Biclustering methods search for sample-variable associations in the form of distinguished submatrices of the data matrix. (The rows and columns of a submatrix need not be contiguous.) In this paper we propose and evaluate a statistically motivated biclustering procedure (LAS) that finds large average submatrices within a given real-valued data matrix. The procedure operates… CONTINUE READING

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