Mathematics, Linear Algebra, Machine Learning, Data Science.
useful in reducing noise and computational complexity in large datasets. The reduced dimensions can capture most of the important information in the data. The key idea behind using SVD for matrix approximation and low-rank representation is that you can retain only the first k singular values and