Results 11 to 20 of about 328,340 (188)
Linear parameter-varying subspace identification: A unified framework
Automatica, 2021 In this paper, we establish a unified framework for subspace identification (SID) of linear parameter-varying (LPV) systems to estimate LPV state-space (SS) models in innovation form. This framework enables us to derive novel LPV SID schemes that are extensions of existing linear time-invariant (LTI) methods. More specifically, we derive the open-loop,
Cox, Pepijn Bastiaan, Tóth, Roland
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Linear subspaces of hypersurfaces [PDF]
Duke Mathematical Journal, 2021 Let $X$ be an arbitrary smooth hypersurface in $\mathbb{C} \mathbb{P}^n$ of degree $d$. We prove the de Jong-Debarre Conjecture for $n \geq 2d-4$: the space of lines in $X$ has dimension $2n-d-3$. We also prove an analogous result for $k$-planes: if $n \geq 2 \binom{d+k-1}{k} + k$, then the space of $k$-planes on $X$ will be irreducible of the expected
Beheshti, Roya, Riedl, Eric
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( λ , μ ) -Fuzzy linear subspaces [PDF]
Journal of Inequalities and Applications, 2013 In this paper, we first introduce the concepts of -fuzzy subfields. Then we generalize the concepts of fuzzy linear spaces, we define
Feng, Yuming, Li, Chuandong
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Subspace‐by‐subspace preconditioners for structured linear systems [PDF]
Numerical Linear Algebra with Applications, 1999 Preconditioners for \(n\) by \(n\) real symmetric positive definite linear systems of equations \(Ax=b\) are constructed under the assumption \(A=\sum _{i=1}^e E_i\), each element \(E_i\) is positive semi-definite. First, element-by-element (EBE) preconditioners are reminded, see, e.g., \textit{T.~J.~R. Hughes, I.~Levit} and \textit{J.~Winget} [Comput.
Michel J. Daydé +2 more
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Subspace-hypercyclic abelian linear semigroups
Journal of Mathematical Analysis and Applications, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Herzi, Salah, Marzougui, Habib
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On Projections to Linear Subspaces
, 2022 The merit of projecting data onto linear subspaces is well known from, e.g., dimension reduction. One key aspect of subspace projections, the maximum preservation of variance (principal component analysis), has been thoroughly researched and the effect of random linear projections on measures such as intrinsic dimensionality still is an ongoing effort.
Erik Thordsen, Erich Schubert
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On relationships between two linear subspaces and two orthogonal projectors
Special Matrices, 2019 Sum and intersection of linear subspaces in a vector space over a field are fundamental operations in linear algebra. The purpose of this survey paper is to give a comprehensive approach to the sums and intersections of two linear subspaces and their ...
Tian Yongge
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Convolutional Subspace Clustering Network With Block Diagonal Prior
IEEE Access, 2020 Standard methods of subspace clustering are based on self-expressiveness in the original data space, which states that a data point in a subspace can be expressed as a linear combination of other points. However, the real data in raw form are usually not
Junjian Zhang +4 more
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Efficient illumination independent appearance-based face tracking [PDF]
, 2009 One of the major challenges that visual tracking algorithms face nowadays is being able to cope with changes in the appearance of the target during tracking. Linear subspace models have been extensively studied and are possibly the most popular way of
Baker +25 more
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Linear symplectomorphisms asR-Lagrangian subspaces [PDF]
Involve, a Journal of Mathematics, 2015 The graph of a real symplectic linear transformation is an R-Lagrangian subspace of a complex symplectic vector space. The restriction of the complex symplectic form is thus purely imaginary and may be expressed in terms of the generating function of the transformation.
Hellmann, Chris +2 more
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