Dimensionality Reduction: Introduction and Motivation

This lecture introduces the concept of dimensionality reduction, focusing on the challenges posed by high-dimensional data representations in machine learning applications such as image processing and genomics. The instructor explains the need to reduce the number of variables to enhance model performance, discussing methods like principal component analysis (PCA) for achieving more compact and robust data representations. The curse of dimensionality is explored, highlighting how high-dimensional spaces lead to data sparsity and increased computational costs. Practical implications of dimensionality reduction, including improved model quality and avoidance of overfitting, are also discussed.