Data Science, Machine Learning, Pca, Big Data, Principal Component.
Stochastic Gradient Descent This is an iterative approach, for when the matrix X is very big. On each iteration, it picks a random example i and features j and updates W and Z as PCA in action: Feature Reduction We already know that, by definition, PCA eliminates the less important features and