Non-Negative Matrix Factorization (NMF) for Dimensionality Reduction in Image Data

I have already discussed different types of dimensionality reduction techniques in detail. Principal Component Analysis (PCA), Factor Analysis (FA), Linear Discriminant Analysis (LDA), Autoencoders (AEs), and Kernel PCA are the most popular ones. Non-Negative Matrix Factorization (NMF or NNMF) is also a linear dimensionality reduction technique that can be used to reduce the dimensionality of the feature matrix. All dimensionality reduction techniques fall under the category of unsupervised machine learning in which we can reveal hidden patterns and important relationships in the data without requiring labels. So, dimensionality reduction algorithms deal with unlabeled data. When training such an algorithm, the fit() method only needs the feature matrix, X as the input and it does not require the label column, y. As its name implies, non-negative matrix factorization (NMF) needs the feature matrix to be non-negative. Because of this non-negativity constraint, the usage of NMF is limited to data with non-negative values such as image data (pixel values always lie between 0 and 255, hence there are no negative values in image data!). <a href="https://towardsdatascience.com/non-negative-matrix-factorization-nmf-for-dimensionality-reduction-in-image-data-8450f4cae8fa">Read More</a>