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Evaluating the statistical significance of biclusters
Biclustering (also known as submatrix localization) is a problem of high practical relevance in exploratory analysis of high-dimensional data. We develop a framework for performing statistical inference on biclusters found by score-based algorithms. Since the bicluster was selected in a data dependent manner by a biclustering or localization algorithm, this is a form of selective inference. Our framework gives exact (non-asymptotic) confidence intervals and p-values for the significance of the selected biclusters.
Country:
- North America > United States > California > Santa Clara County > Stanford (0.04)
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
Technology: Information Technology > Artificial Intelligence > Representation & Reasoning > Search (0.70)