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Finding large average submatrices in high dimensional data
2009
Annals of Applied Statistics
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
doi:10.1214/09-aoas239
fatcat:mnznvgq3ozdx7kf65ascv7yidi