@inproceedings{43b8077a7a59418090e9e1296cca27fe,
title = "Fast detection of block boundaries in block-wise constant matrices",
abstract = "We propose a novel approach for estimating the location of block boundaries (change-points) in a random matrix consisting of a block wise constant matrix observed in white noise. Our method consists in rephrasing this task as a variable selection issue. We use a penalized least-squares criterion with an ℓ1-type penalty for dealing with this problem. We first provide some theoretical 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 approach to data coming from molecular biology which can be used for better understanding the structure of the chromatin.",
keywords = "Change-points, HiC experiments, High-dimensional sparse linear model",
author = "Vincent Brault and Julien Chiquet and C{\'e}line L{\'e}vy-Leduc",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016 ; Conference date: 16-07-2016 Through 21-07-2016",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-319-41920-6\_16",
language = "English",
isbn = "9783319419190",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "214--228",
editor = "Petra Perner",
booktitle = "Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings",
}