Abstract
Bi-clustering refers to the task of partitioning the rows and columns of a data matrix simultaneously. Although empirically useful, the theoretical aspects of bi-clustering techniques have not been studied in-depth. We present a framework for investigating the statistical guarantees behind the sparse bi-clustering algorithm by using the Vapnik–Chervonenkis (VC) theory.
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