Svd, Machine Learning, Data Science, Artificial Intelligence, Matrix Factorization.
that have the highest variance. PCA involves computing the eigenvectors of the covariance matrix of the data and retaining the top k eigenvectors that capture the most information. PCA is useful for reducing the number of features in a dataset while retaining the most important information.