Results 141 to 150 of about 123,245 (174)

The iterative methods for computing invariant subspaces and their applications

, 1987
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Mesh variational r-adaptivity for sharp modeling of brittle fracture

Dony G, Moës N, Remacle J.
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Invariant Subspace Method and Exact Solutions of Certain Nonlinear Time Fractional Partial Differential Equations

Fractional Calculus and Applied Analysis, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sahadevan, Ramajayam, Bakkyaraj, Thangarasu   +1 more
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A combined invariant-subspace and subspace identification method for continuous-time state–space models using slowly sampled multi-sine-wave data

Automatica, 2022
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Chao Huang
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Invariant subspace method for eigenvalue computation

IEEE Transactions on Power Systems, 1993
Methods which first subdivide a large power system into subsystems, and which then study how the interactions between subsystems cause the eigenvalues and eigenvectors to vary between the subsystems and the total system are presented. The invariant subspace method, which allows eigenvalues that are difficult to study individually to be grouped into a ...
D.J. Stadnicki, J.E. Van Ness
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Linear-quadratic mean field control: The invariant subspace method

Automatica, 2019
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Chen, Xiang, Huang, Minyi
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Kernel-based invariant subspace method for hyperspectral target detection

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004
In this paper, a kernel-based invariant subspace detection method is proposed for small target detection of hyperspectral images. The method combines kernel principal component analysis (KPCA) and the linear mixture model (LMM). The LMM is used to describe each pixel in the hyper-spectral image as a mixture of target, background and noise.
null Ye Zhang, null Yanfeng Gu
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A Supervised Low-Rank Method for Learning Invariant Subspaces

2015 IEEE International Conference on Computer Vision (ICCV), 2015
Sparse representation and low-rank matrix decomposition approaches have been successfully applied to several computer vision problems. They build a generative representation of the data, which often requires complex training as well as testing to be robust against data variations induced by nuisance factors.
Farzad Siyahjani, Ranya Almohsen, Sinan Sabri, Gianfranco Doretto   +3 more
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