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We propose a novel approach for estimating the location of block boundaries(change-points) in a random matrix consisting of a block wise constant matrixobserved in white noise. Our method consists in rephrasing this task as avariable selection issue. We use a penalized least-squares criterion with an$\ell_1$-type penalty for dealing with this issue. We first provide sometheoretical results ensuring the consistency of our change-point estimators.Then, we explain how to implement our method in a very efficient way. Finally,we provide some empirical evidence to support our claims and apply our approachto HiC data which are used in molecular biology for better understanding theinfluence of the chromosomal conformation on the cells functioning.

Fast Detection of Block Boundaries in Block Wise Constant Matrices: An Application to HiC data