Fast Detection of Block Boundaries in Block-Wise Constant Matrices

被引：0

作者：

Brault, Vincent ^{[1
]}

Chiquet, Julien ^{[1
]}

Levy-Leduc, Celine ^{[1
]}

机构：

[1] Univ Paris Saclay, INRA, AgroParisTech, UMR MIA Paris, F-75005 Paris, France

来源：

MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION (MLDM 2016) | 2016年 / 9729卷

关键词：

Change-points; High-dimensional sparse linear model; HiC experiments; PATH;

D O I：

10.1007/978-3-319-41920-6_16

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 l(1)-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.

引用

页码：214 / 228

页数：15