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singular value
This term is a specialized technical expression used primarily in linear algebra and data science. It describes the scaling factor associated with the principal axes of a linear transformation, providing a measure of the importance or variance of a specific dimension within a dataset.
In practical applications like Principal Component Analysis (PCA) or image compression, singular values are typically ordered from largest to smallest. This allows practitioners to identify which components contribute most to the structure of the matrix and which can be discarded as noise.
Meanings
A non-negative square root of an eigenvalue of the matrix AA (or A transpose A), representing the magnitude of the transformation along a specific principal axis in singular value decomposition.
The singular value of a matrix indicates the amount of variance captured by its corresponding singular vector.