Manifold Learning

Manifold learning is a technique in data analysis that seeks to understand complex, high-dimensional data by finding simpler, lower-dimensional structures, or "manifolds," within it. Imagine a large, complicated landscape (the high-dimensional data) that can be mapped onto a simpler, flatter surface (the lower-dimensional space). By uncovering these patterns, manifold learning helps reveal the underlying relationships and features in the data, making it easier to visualize and analyze. This approach is especially useful in fields like machine learning, where understanding intricate data shapes can lead to better predictions and insights.