Best multilinear rank approximation of tensors with quasi-Newton methods on Grassmannians
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Summary
Algorithms for the reduced rank regression problem and algorithms for the computation of the best multilinear rank approximation of tensors are discussed and an algorithm that solves the generalized problem and identify properties of the input and output signals causing a singular objective matrix is given.
- Type
- article
- Published
- 2008-01-01
- Cited by
- 26
- References
- 39
- OpenAlex
- https://openalex.org/W1495149611
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:18519695
Keywords
Multilinear map, Rank (graph theory), Mathematics, Low-rank approximation, Singular value
References
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- Subspace Computations via Matrix Decompositions and Geometric Optimization
- ARPACK users' guide - solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods
- Dimensionality reduction for higher-order tensors: algorithms and applications
- Numerical methods for unconstrained optimization and nonlinear equations
- Some mathematical notes on three-mode factor analysis
- Minimizing a differentiable function over a differential manifold
- A Multilinear Singular Value Decomposition
- Factorized variable metric methods for unconstrained optimization
- On the Best Rank-1 and Rank-(R1 , R2, ... , RN) Approximation of Higher-Order Tensors
- Decomposition of quantics in sums of powers of linear forms
- An introduction to differentiable manifolds and Riemannian geometry
- A Newton-Grassmann Method for Computing the Best Multilinear Rank-(r1, r2, r3) Approximation of a Tensor
- Symmetric Tensors and Symmetric Tensor Rank
- The Geometry of Algorithms with Orthogonality Constraints
- Representations of quasi-Newton matrices and their use in limited memory methods
- A Modified Cholesky Algorithm Based on a Symmetric Indefinite Factorization
- On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors
- Multi-Way Analysis: Applications in the Chemical Sciences
Cited by
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- Algorithms in data mining using matrix and tensor methods
- Numerical Optimization Methods on Riemannian Manifolds
- An Efficient BFGS Algorithm for Riemannian Optimization
- Optimization on manifolds: methods and applications (abstract)
- Exponential data fitting using multilinear algebra: the decimative case
- A Newton-Grassmann Method for Computing the Best Multilinear Rank-(r1, r2, r3) Approximation of a Tensor
- Differential-geometric Newton method for the best rank-(R1, R2, R3) approximation of tensors
- Tucker compression and local optima
- HRTF customization using multiway array analysis
- A survey of tensor methods
- Low multilinear rank tensor approximation via semidefinite programming
- Fast Evaluation of Near-Field Boundary Integralsusing Tensor Approximations
- The best low multilinear rank approximation of tensors : some practical aspects
- Numerical Methods for the Best Low Multilinear Rank Approximation of Higher-Order Tensors (Numerieke methoden voor de beste lage multilineaire rang benadering van hogere-orde tensoren)
- Overview of recent advances in numerical tensor algebra
- Development and Evaluation of an Immersive Audio Conferencing System
- The properties of partial trace and block trace operators of partitioned matrices
- TECHNIQUES FOR THE DECOMPOSITION OF CARTAN’S CURVATURE TENSOR IN COMPLEX FINSLER MANIFOLDS
- Toolbox for Grassmann Manifold Computations
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