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Results tagged with svd
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user 411246
Singular value decomposition (SVD) of a matrix $\mathbf{A}$ is given by $\mathbf{A} = \mathbf{USV}^\top$ where $\mathbf{U}$ and $\mathbf{V}$ are orthogonal matrices and $\mathbf{S}$ is a diagonal matrix.
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Are there any situations where orthogonality is not optimal?
Popular data reduction techniques, such as SVD or PCA map/project high-dimensional data to a lower-dimensional representational space, where the dimensions are mutually orthogonal. … Are there any situations where applying SVD or PCA to have the assurance of orthogonality is not just the best thing to be doing? …
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