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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Visualizing Large-scale and High-dimensional Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1602.00370/</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/1602.00370/feed.xml" rel="self" type="application/rss+xml" /><item><title>Visualizing Large-scale and High-dimensional Data — mentioned by agibsonccc</title><link>https://news.ycombinator.com/item?id=16350392</link><guid isPermaLink="false">urn:hackerpapers:mention:1602.00370:16350392</guid><pubDate>Sun, 11 Feb 2018 00:12:25 GMT</pubDate><description>&lt;p&gt;For those interested in this space, folks might like some of the work that went in to visualizing T Distributed Stochastic Neighbor Embedding and the successor to that LargeVis which use multiple forms of KNN trees each with their own trade offs: https://arxiv.org/abs/1301.3342 (Barnes Hut) https://arxiv.org/abs/1602.00370 (LargeVis) One uses Vantage Point Trees which sudivides the space in to quadrants, and the other uses Random Projection Trees. Both are interesting exercises in using KNN like techniques to visualize high dimensional spaces.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1602.00370/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Visualizing Large-scale and High-dimensional Data — HN submission</title><link>https://news.ycombinator.com/item?id=11656133</link><guid isPermaLink="false">urn:hackerpapers:submission:1602.00370:11656133</guid><pubDate>Sun, 08 May 2016 21:56:53 GMT</pubDate><description>&lt;p&gt;Visualizing Large-Scale and High-Dimensional Data&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;92 points; 18 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1602.00370/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
