Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction while preserving as much variability in the data as possible. It is widely used in fields such as….
Tag: Covariance Matrix
Gaussian Mixture Models
Gaussian Mixture Models (GMM) – A Comprehensive Guide 1. Introduction to Gaussian Mixture Models (GMM) A Gaussian Mixture Model (GMM) is a probabilistic clustering algorithm based on the assumption that….
Using Principal Component Analysis (PCA)
Principal Component Analysis (PCA) – A Comprehensive Guide Introduction to PCA Principal Component Analysis (PCA) is a powerful dimensionality reduction technique used in machine learning and data science. It transforms….