According to the description of SVD algorithm, the elements along the diagonal of $\sum$ are known as the singular values.

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Singular value decomposition (SVD) is an unsupervised dimensionality reduction technique used for feature extraction.  The singular value decomposition of an m x n matrix M is a factorization of the form U$$\sum$$M*.  U is an m x m unitary matrix. $$\sum$$ is a m x n diagonal matrix. V is a v x v unitary matrix.
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Singular value decomposition (SVD)

Goodfellow, I., Bengio, Y., & Courville, A. (2016). $\mathit{Deep \ Learning.}$ MIT Press. Retrieved from [www.deeplearningbook.org](https://www.deeplearningbook.org) 

Deep Learning

Kalman, Dan. (1/1/1996). A Singularly Valuable Decomposition: The SVD of a Matrix. The College Mathematics Journal, 27(1), 2–23. Original Articles, Taylor & Francis.

A Singularly Valuable Decomposition: The SVD of a Matrix

According to the description of SVD algorithm, the columns of U are known as Left-singular Vectors.

Left-singular Vectors

According to the description of SVD algorithm, the columns of V are known as Right-singular Vectors.

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