<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Challenges on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/challenges/</link><description>Recent content in Challenges on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/challenges/index.xml" rel="self" type="application/rss+xml"/><item><title>high-dimensional</title><link>https://terms-en.ai-term-hub.com/en/terms/high_dimensional/</link><pubDate>Sat, 18 Jul 2026 09:38:34 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/high_dimensional/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>High-dimensional refers to datasets or vector spaces containing a vast number of attributes or features. In AI, this is common in text embeddings, image pixels, or gene expression data. While rich in information, high dimensionality can cause the &amp;lsquo;curse of dimensionality,&amp;rsquo; where data becomes sparse, distances between points lose meaning, and models require significantly more data and computational power to learn effectively.&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>Describes data spaces with a large number of features or dimensions, often leading to sparsity and computational challenges.&lt;/p></description></item></channel></rss>