Biclustering consists in simultaneous partitioning of the set of samples and the set of their attributes (features) into subsets (classes). Samples and features classified together are supposed to have a high relevance to each other which can be observed by intensity of their expressions. We define the notion of consistency for biclustering using interrelation between centroids of sample and feature classes. We prove that consistent biclustering implies separability of the classes by convex cones. While previous works on biclustering concentrated on unsupervised learning and did not consider employing a training set, whose classification is given, we propose a model for supervised biclustering, whose consistency is achieved by feature selection. The developed model involves solution of a fractional 0–1 programming problem. Preliminary computational results on microarray data mining problems are reported.
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This research work was partially supported by NSF, NIH and AirForce grants.
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Busygin, S., Prokopyev, O.A. & Pardalos, P.M. Feature Selection for Consistent Biclustering via Fractional 0–1 Programming. J Comb Optim 10, 7–21 (2005). https://doi.org/10.1007/s10878-005-1856-y
- feature selection
- supervised learning