What Is Principal Component Analysis (PCA) in Machine Learning?

Principal Component Analysis (PCA) is a common way to obtain embeddings that does not rely on neural networks. It comes from a family of dimensionality reduction and matrix factorization techniques and can operate efficiently on huge amounts of data.