PCA and NMF are both dimension reduction methods that can be used for BIG DATA problems. They use matrix algebra to transform the original feature space in the dataset to a much lower dimensional space while keeping the majority of the information in the dataset. To make this article more vivid, I would like to use image data to show the functionality and difference about these two methods. In fact, PCA and NMF are very natural transformation methods for image data, because image data has large feature space (width x height x channel) and pixels in the image are also highly correlated. Let’s take a look at this flower dataset first. The flower data set I used in this article was from Kaggle ( https://www.kaggle.com/alxmamaev/flowers-recognition ) We have roses and sunflowers in this image dataset. Each image is represented as a 128x128x3 numpy array. So if you flatten each image, you will get a 1D array with 49152 features, which is a REALLY LARGE number in terms of features....
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