The SVD stands for Singular Value Decomposition. After decomposing a data matrix $\mathbf X$ using SVD, it results in three matrices, two matrices with the singular vectors $\mathbf U$ and $\mathbf V$, and one singular value matrix whose diagonal elements are the singular values. But I want to know why those values are named as singular values. Is there any connection between a singular matrix and these singular values?
[Math] Why the SVD is named so…
linear algebramath-historysvdterminology
Best Answer
From On the Early History of the Singular Value Decomposition by Pete Stewart:
See the paper for more fascinating accounts on how SVD came to be, even before the seminal paper of Golub/Kahan.