Background: The basic set-up for non-negative matrix factorization, nmf, is that we take a matrix with non-negative elements, X and find two other non-negative matrices H and T such that $||X - HT||$ is a minimum. $X$ in this example is a matrix that represents a data set. Rows correspond to samples of data, and columns correspond to features. I understand that nmf is used for feature reduction. My understanding is that you can use the latent features to train a model on. However, **suppose that I use nmf for feature reduction and then get a new row of data and want to find the reduced features for the new data. Do I have to run nmf again with an updated dataset and then retrain my model each time?**