Linear Algebra for Data Science
In the context of Singular Value Decomposition (SVD), σ represents the singular values of a matrix. These values are crucial in understanding the properties of the matrix and help in dimensionality reduction, data compression, and noise reduction. The singular values are always non-negative and ordered from largest to smallest, reflecting the importance of each corresponding singular vector in capturing the structure of the data represented by the matrix.
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