Svd, Matrix Decomposition, Data Processing, Feature Engineering, Unsupervised Learning.
which would you pick? One perspective is to use the feature with the highest variance / spread as it will capture most of the variance in the data (thus approximating it the best and being the closest to the original data as possible). Notice how the data projected onto A is much more similar to