Dimension Reduction: Curse of Dimensionality

This lecture introduces the concept of dimension reduction, focusing on the curse of dimensionality. It covers the organization of data, the purpose of dimension reduction, and the implications of high dimensionality. The curse of dimensionality is explained through the exponential relation between the number of examples and the dimension. Various reasons for dimension reduction are discussed, including algorithmic cost reduction, model quality improvement, and visualization facilitation. The lecture also explores the neighborhood concept in high dimensions and different approaches to dimensionality reduction, such as variable extraction and selection.