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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cauchy Principal Component Analysis · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1412.6506/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1412.6506/feed.xml" rel="self" type="application/rss+xml" /><item><title>Cauchy Principal Component Analysis — mentioned by yummyfajitas</title><link>https://news.ycombinator.com/item?id=9196407</link><guid isPermaLink="false">urn:hackerpapers:mention:1412.6506:9196407</guid><pubDate>Fri, 13 Mar 2015 10:25:10 GMT</pubDate><description>&lt;p&gt;So the CLT says the sum of random variables with rapidly decaying tails will approach a normal distribution. There are similar results showing that the sum of slowly decaying random variables approaches a stable distribution: https://en.wikipedia.org/wiki/Stable_distribution This makes the stable distribution the right answer under some circumstances. For different test statistics (e.g. max drawdown), you&amp;#x27;ve got similar fat tailed distributions, e.g. GEV: https://en.wikipedia.org/wiki/Generalized_extreme_value_dist... As an example of how you might use slowly decaying distributions, consider…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1412.6506/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
