Dimensionality reduction is a crucial technique in machine learning and data analysis that aims to reduce the number of random variables under consideration and avoid The Curse of Dimensionality by obtaining a set of principal variables. This process helps in simplifying models, reducing computational costs, and improving the interpretability of the data. Here are some key aspects and techniques of dimensionality reduction.
Density-Based Spatial Clustering of Applications with Noise