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In the biclustering problem, we seek to simultaneously group observations and features. While biclustering has applications in a wide array of domains, ranging from text mining to collaborative filtering, the problem of identifying structure in high‐dimensional genomic data motivates this work. In this context, biclustering enables us to identify subsets of genes that are co‐expressed only within...
Various model selection methods can be applied to seek sparse subsets of the covariates to explain the response of interest in bioinformatics. While such methods often offer very helpful predictive performances, their selections of the covariates may be much less trustworthy. Indeed, when the number of covariates is large, the selections can be highly unstable, even under a slight change of the data...
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