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<h5>Description:</h5>
<p>We provide a procedure to obtain distribution-free confidence intervals on the eigendecomposition of covariance matrices by using U-statistics, Weyl&rsquo;s theorem and the Eigenvalue-eigenvector identity.</p>

<h5>More Information:</h5>
<ul>
<li><strong>Grand Challenge/Workpackage(s)</strong>: <a href="/nl/onderzoek/onderzoeksuitdagingen/ai-driven-data-science" data-entry-id="58" target="_blank" rel="noopener" data-entry-id="58">GC1</a> - WP6</li>
<li><strong>Responsible Research Lead</strong>: <a href="mailto:matthew.blaschko@esat.kuleuven.be" target="_blank" rel="noopener">Matthew B. Blaschko</a></li>
<li><strong>Authors</strong>: <a href="mailto:teodora.popordanoska@kuleuven.be" target="_blank" rel="noopener">Teodora Popordanoska</a>, <a href="mailto:aleksei.tiulpin@aalto.fi" target="_blank" rel="noopener">Aleksei Tiulpin</a>, <a href="mailto:wacha.bounliphone@gmail.com" target="_blank" rel="noopener">Wacha Bounliphone</a>, <a href="mailto:matthew.blaschko@esat.kuleuven.be" target="_blank" rel="noopener">Matthew B. Blaschko</a>&nbsp;</li>
<li><strong>Programming Language</strong>: Python</li>
<li><a href="https://github.com/tpopordanoska/confidence-intervals" target="_blank" rel="noopener">Link to the repository</a></li>
<li><strong>Categories:</strong> Unsupervised Learning</li>
</ul>

<h5>Publications:</h5>
<ul>
<li>T. Popordanoska, A. Tiulpin, W. Bounliphone M. Blaschko, "<a href="https://limo.libis.be/primo-explore/fulldisplay?docid=LIRIAS3511124&amp;context=L&amp;vid=Lirias&amp;search_scope=Lirias&amp;tab=default_tab&amp;lang=en_US&amp;fromSitemap=1" target="_blank" rel="noopener">Distribution-independent confidence intervals for the eigendecomposition of covariance matrices via the eigenvalue-eigenvector identity</a>", ICML 2021 workshop on distribution-free uncertainty quantification, 2021</li>
</ul>

<p>A procedure for calculating confidence intervals for the eigendecomposition of covariance matrices.</p>