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1. Singular value decomposition: In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any \displaystyle{ m \times n }[/math] matrix. (Matrix decomposition) [100%] 2021-12-22 [Singular value decomposition] [Linear algebra]...
2. Singular value decomposition: In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any m × n {\displaystyle m\times n} matrix. (Matrix decomposition) [100%] 2021-12-22 [Singular value decomposition] [Linear algebra]...
3. Generalized singular value decomposition: In linear algebra, the generalized singular value decomposition (GSVD) is the name of two different techniques based on the singular value decomposition. The two versions differ because one version decomposes two (or more) matrices (much like higher order PCA) and ... (Name of two different techniques based on the singular value decomposition) [86%] 2022-07-04 [Linear algebra] [Singular value decomposition]...
4. Higher-order singular value decomposition: In multilinear algebra, the higher-order singular value decomposition (HOSVD) of a tensor is a specific orthogonal Tucker decomposition. It may be regarded as one generalization of the matrix singular value decomposition. (Tensor decomposition) [77%] 2022-12-23 [Multilinear algebra] [Tensors]...
5. Two-dimensional singular-value decomposition: Two-dimensional singular-value decomposition (2DSVD) computes the low-rank approximation of a set of matrices such as 2D images or weather maps in a manner almost identical to SVD (singular-value decomposition) which computes the low-rank approximation of ... [77%] 2023-08-01 [Singular value decomposition]
6. Two-dimensional singular-value decomposition: Two-dimensional singular-value decomposition (2DSVD) computes the low-rank approximation of a set of matrices such as 2D images or weather maps in a manner almost identical to SVD (singular-value decomposition) which computes the low-rank approximation of ... [77%] 2023-01-12 [Singular value decomposition]
7. Higher-order singular value decomposition: In multilinear algebra, the higher-order singular value decomposition (HOSVD) of a tensor is a specific orthogonal Tucker decomposition. It may be regarded as one type of generalization of the matrix singular value decomposition. (Tensor decomposition) [77%] 2024-09-06 [Multilinear algebra] [Tensors]...