Svd, Matrix Decomposition, Data Processing, Feature Engineering, Unsupervised Learning.
singular value so that the feature that explains the most variance in the data is the first columns of U. To visualize just how much variance in the data is explained by the singular vectors let’s use the following image of a boat as our matrix of interest. The image has 512 columns and rows and it