In singular value decomposition (SVD), the elements along the diagonal of the matrix $$\Sigma$$ are known as the singular values.

University of Michigan - Ann Arbor

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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

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.

Right-singular Vectors

Kalman, D. (1996). A singularly valuable decomposition: The SVD of a matrix. The College Mathematics Journal, 27(1), 2–23.

Kalman (1996)

Singular Values

In relation-based knowledge transfer, feature map relationships between two neural network layers can be modeled using a Gram matrix. This matrix summarizes relations by calculating the inner products between pairs of feature maps from the two layers. Singular Value Decomposition (SVD) is then applied to the resulting Gram matrix to extract essential information from these feature map correlations.

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