Results 21 to 30 of about 328,340 (188)
The influence of different PAST-based subspace trackers on DaPT parameter estimation [PDF]
Advances in Radio Science, 2012 In the context of parameter estimation, subspace-based methods like ESPRIT have become common. They require a subspace separation e.g. based on eigenvalue/-vector decomposition. In time-varying environments, this can be done by subspace trackers.
M. Lechtenberg, J. Götze
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IEEE Access, 2021 As the state-of-the-art technology of Bayesian inference, based on low-dimensional principal components analysis (PCA) subspace inference methods can provide approximately accurate predictive distribution and well calibrated uncertainty.
Yongguang Wang, Shuzhen Yao, Tian Xu
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Face Recognition Using Classification-Based Linear Projections
EURASIP Journal on Advances in Signal Processing, 2008 Subspace methods have been successfully applied to face recognition tasks. In this study we propose a face recognition algorithm based on a linear subspace projection. The subspace is found via utilizing a variant of the neighbourhood component analysis (
Jacob Goldberger, Moshe Butman
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Invariant subspaces of nilpotent linear operators, I [PDF]
Journal für die reine und angewandte Mathematik (Crelles Journal), 2008 Let $k$ be a field. We consider triples $(V,U,T)$, where $V$ is a finite dimensional $k$-space, $U$ a subspace of $V$ and $T \:V \to V$ a linear operator with $T^n = 0$ for some $n$, and such that $T(U) \subseteq U$. Thus, $T$ is a nilpotent operator on $V$, and $U$ is an invariant subspace with respect to $T$.
Ringel, Claus Michael +1 more
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On the extension of positive linear functionals
International Journal of Mathematics and Mathematical Sciences, 2000 A necessary and sufficient condition to extend a continuous linear real functionals which is positive with respect to a semi-group defined on a subspace of a linear space is discussed in this paper.
Ergashboy Muhamadiev, Adel T. Diab
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Matched direction detectors [PDF]
, 2005 In this paper, we address the problem of detecting a signal whose associated spatial signature is subject to uncertainties, in the presence of subspace interference and broadband noise, and using multiple snapshots from an array of sensors.
Besson, Olivier +2 more
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IEEE Access, 2018 With the increasing demand for unsupervised learning for fault diagnosis, the subspace clustering has been considered as a promising technique enabling unsupervised fault diagnosis. Although various subspace clustering methods have been developed to deal
Jie Gao +4 more
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Subspace-diskcyclic sequences of linear operators [PDF]
Sahand Communications in Mathematical Analysis, 2017 A sequence ${T_n}_{n=1}^{infty}$ of bounded linear operators on a separable infinite dimensional Hilbert space $mathcal{H}$ is called subspace-diskcyclic with respect to the closed subspace $Msubseteq mathcal{H},$ if there exists a vector $xin mathcal{H}
Mohammad Reza Azimi
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SSA of biomedical signals: A linear invariant systems approach [PDF]
, 2010 Singular spectrum analysis (SSA) is considered from a linear invariant systems perspective. In this terminology, the extracted components are considered as outputs of a linear invariant system which corresponds to finite impulse response (FIR) filters ...
Figueiredo, Nuno +5 more
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Neural Subspace Learning for Surface Defect Detection
Mathematics, 2022 Surface defect inspection is a key technique in industrial product assessments. Compared with other visual applications, industrial defect inspection suffers from a small sample problem and a lack of labeled data.
Bin Liu +3 more
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