Non-negative matrix factorization (NMF) vs Principal Component Analysis (PCA)

In the field of data analysis and dimensionality reduction, Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA) are two powerful techniques that play an important role in uncovering patterns, reducing noise, and extracting essential features from complex datasets.

Both methods have found applications in various domains, including image processing, text mining, genetics, and more. In this comprehensive exploration, we will delve into the intricacies of NMF and PCA, high...