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  2. Tensor rank decomposition - Wikipedia

    en.wikipedia.org/wiki/Tensor_rank_decomposition

    The rank of a tensor depends on the field over which the tensor is decomposed. It is known that some real tensors may admit a complex decomposition whose rank is strictly less than the rank of a real decomposition of the same tensor. As an example, [8] consider the following real tensor

  3. Raising and lowering indices - Wikipedia

    en.wikipedia.org/wiki/Raising_and_lowering_indices

    2.3 General rank. 3 See also. ... Download QR code; Print/export Download as PDF; ... (1,1) tensor is a linear map. An example is the delta, ...

  4. Higher-order singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Higher-order_singular...

    Some aspects can be traced as far back as F. L. Hitchcock in 1928, [1] but it was L. R. Tucker who developed for third-order tensors the general Tucker decomposition in the 1960s, [2] [3] [4] further advocated by L. De Lathauwer et al. [5] in their Multilinear SVD work that employs the power method, or advocated by Vasilescu and Terzopoulos ...

  5. Tensor - Wikipedia

    en.wikipedia.org/wiki/Tensor

    The tensors are classified according to their type (n, m), where n is the number of contravariant indices, m is the number of covariant indices, and n + m gives the total order of the tensor. For example, a bilinear form is the same thing as a (0, 2)-tensor; an inner product is an example of a (0, 2)-tensor, but not all (0, 2)-tensors are inner ...

  6. Rank (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Rank_(linear_algebra)

    Thinking of matrices as tensors, the tensor rank generalizes to arbitrary tensors; for tensors of order greater than 2 (matrices are order 2 tensors), rank is very hard to compute, unlike for matrices. There is a notion of rank for smooth maps between smooth manifolds. It is equal to the linear rank of the derivative.

  7. Singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Singular_value_decomposition

    ⁠ For example, in the above example the null space is spanned by the last row of ⁠ ⁠ and the range is spanned by the first three columns of ⁠. As a consequence, the rank of ⁠ M {\displaystyle \mathbf {M} } ⁠ equals the number of non-zero singular values which is the same as the number of non-zero diagonal elements in Σ ...

  8. CUR matrix approximation - Wikipedia

    en.wikipedia.org/wiki/CUR_matrix_approximation

    Tensor-CURT decomposition [6] is a generalization of matrix-CUR decomposition. Formally, a CURT tensor approximation of a tensor A is three matrices and a (core-)tensor C, R, T and U such that C is made from columns of A, R is made from rows of A, T is made from tubes of A and that the product U(C,R,T) (where the ,,-th entry of it is ′, ′, ′ ′, ′, ′, ′, ′, ′) closely ...

  9. Tensor decomposition - Wikipedia

    en.wikipedia.org/wiki/Tensor_decomposition

    A multi-way graph with K perspectives is a collection of K matrices ,..... with dimensions I × J (where I, J are the number of nodes). This collection of matrices is naturally represented as a tensor X of size I × J × K. In order to avoid overloading the term “dimension”, we call an I × J × K tensor a three “mode” tensor, where “modes” are the numbers of indices used to index ...