<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ℓ1,2 Norm |</title><link>https://www.khaichiong.com/tags/%E2%84%9312-norm/</link><atom:link href="https://www.khaichiong.com/tags/%E2%84%9312-norm/index.xml" rel="self" type="application/rss+xml"/><description>ℓ1,2 Norm</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Mon, 01 Oct 2018 00:00:00 +0000</lastBuildDate><image><url>https://www.khaichiong.com/media/icon_hu_da05098ef60dc2e7.png</url><title>ℓ1,2 Norm</title><link>https://www.khaichiong.com/tags/%E2%84%9312-norm/</link></image><item><title>Estimation of Graphical Models using the ℓ₁,₂ Norm</title><link>https://www.khaichiong.com/publications/estimation-graphical-models-l12-norm/</link><pubDate>Mon, 01 Oct 2018 00:00:00 +0000</pubDate><guid>https://www.khaichiong.com/publications/estimation-graphical-models-l12-norm/</guid><description>&lt;p&gt;In this article, Chiong and Moon develop estimation techniques for graphical
models based on the ℓ₁,₂ norm penalty. The method promotes group sparsity in
the inverse covariance matrix, allowing practitioners to uncover network
structures in data sets with many variables. The technique has applications in
econometrics and machine learning.&lt;/p&gt;</description></item></channel></rss>