In this video, we explore the curse of dimensionality, where data becomes increasingly sparse as dimensions increase, causing traditional algorithms to break down. We examine its counterintuitive properties and practical solutions, from dimensionality reduction to specialized algorithms, showing why this phenomenon matters for machine learning.
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*Contents*
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00:00 - Intro
01:11 - Distance Increases with Dimensions
02:27 - Volume Concentrates in Corners
03:59 - Exponential Data Requirements
04:58 - Random Vectors Become Orthogonal
05:56 - Solutions to the Curse
06:57 - The Blessing of Dimensionality
07:29 - Outro
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