Third-Order Tensors as Operators on Matrices: A Theoretical and Computational Framework with Applications in Imaging
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Abstract
Recent work by Kilmer and Martin [Linear Algebra Appl., 435 (2011), pp. 641--658] and Braman [Linear Algebra Appl., 433 (2010), pp. 1241--1253] provides a setting in which the familiar tools of linear algebra can be extended to better understand third-order tensors. Continuing along this vein, this paper investigates further implications including (1) a bilinear operator on the matrices which is nearly an inner product and which leads to definitions for length of matrices, angle between two matrices, and orthogonality of matrices, and (2) the use of t-linear combinations to characterize the range and kernel of a mapping defined by a third-order tensor and the t-product and the quantification of the dimensions…
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Keywords
- Mathematics
- Linear algebra
- Krylov subspace
- Tensor product
- Algebra over a field
- Orthogonalization
- Tensor (intrinsic definition)
- Orthogonality
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