Robust dictionary learning: Application to signal disaggregation
Angshul Majumdar, Rabab Kreidieh Ward
Abstract
It is well known that the Euclidean norm is sensitive to outliers; yet it is widely used for minimizing it is easy. Dictionary learning is no exception - the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm allows for easy update of the basis/dictionary atoms. In this work, we propose a robust dictionary learning method that is based on minimizing the robust l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm. The ensuing optimization is solved using the Split Bregman approach. We apply the proposed technique to signal (energy and water) disaggregation and show that it excels over existing dictionary learning techniques (based on l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm).
BibTeX
@inproceedings{icassp2016_robustdictionary,
title = {Robust dictionary learning: Application to signal disaggregation},
author = {Angshul Majumdar and Rabab Kreidieh Ward},
booktitle = {ICASSP 2016},
year = {2016}
}