Principal component analysis at it's best. Works awfully well on most machine learning problems. Often PCA is not just enough by itself, so you run it before you run a more sophisticated algorithm (say k-Nearest-Neighbors, or Support Vector Machines). It helps in reducing the training time and execution time of those algorithms.