<ul><li>The curse of dimensionality refers to the exponential growth of space as the number of dimensions increases, causing data points to become increasingly sparse.</li><li>As the number of dimensions increases, the chance of finding data points close to each other becomes nearly impossible unless the number of samples is very large.</li><li>Dimensionality reduction techniques like PCA, t-SNE, and autoencoders help simplify high-dimensional data by identifying and retaining the most informative features.</li><li>By reducing dimensions, these techniques mitigate the issues caused by data sparsity and improve computational efficiency, enabling better performance of machine learning algorithms.</li></ul>

Curse of Dimensionality

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