Manifold Hypothesis

The Manifold Hypothesis suggests that although high-dimensional data (like images or sounds) seems complex, it actually exists on or near a much lower-dimensional surface called a "manifold." Imagine a high-dimensional space as a vast, tangled web, but the meaningful data points lie along smooth, lower-dimensional curves or surfaces within it. This idea helps machine learning models focus on the essential structure of data, making tasks like classification or recognition more efficient by understanding the underlying patterns rather than the entire complex space.