Introduction to dimensionality reduction

This post introduces the concept of dimensionality reduction, explaining its purpose in simplifying complex datasets and making them more manageable. It defines 'dimensions' in the context of data (columns/features), illustrates the problem of high dimensionality with examples, and introduces the 'curse of dimensionality.' The post then categorizes dimensionality reduction techniques into linear (PCA, Truncated SVD, ICA) and non-linear (UMAP, t-SNE, Multidimensional Scaling) approaches, highlighting the types of relationships each method is suited for. It also references a companion blog post for practical implementation details.