Results 1 to 10 of about 274,693 (175)
Low-rank matrix approximations over canonical subspaces [PDF]
Journal of Numerical Analysis and Approximation Theory, 2020 In this paper we derive closed form expressions for the nearest rank-\(k\) matrix on canonical subspaces. We start by studying three kinds of subspaces. Let \(X\) and \(Y\) be a pair of given matrices. The first subspace contains all the \(m\times
Achiya Dax
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Perturbation analysis of the canonical subspaces
Linear Algebra and its Applications, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei, Musheng, De Pierro, Alvaro R.
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Asymptotic and Probabilistic Perturbation Analysis of Controllable Subspaces
ComputationIn this paper, we consider the sensitivity of the controllable subspaces of single-input linear control systems to small perturbations of the system matrices.
Vera Angelova +2 more
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Analyzing and Controlling Inter-Head Diversity in Multi-Head Attention
Applied Sciences, 2021 Multi-head attention, a powerful strategy for Transformer, is assumed to utilize information from diverse representation subspaces. However, measuring diversity between heads’ representations or exploiting the diversity has been rarely studied.
Hyeongu Yun, Taegwan Kang, Kyomin Jung
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A vanishing theorem for the canonical blow-ups of Grassmann manifolds
Complex Manifolds, 2021 Let 𝒯 s,p,n be the canonical blow-up of the Grassmann manifold G(p, n) constructed by blowing up the Plücker coordinate subspaces associated with the parameter s. We prove that the higher cohomology groups of the tangent bundle of 𝒯 s,p,n vanish.
Fang Hanlong, Zhu Songhao
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Empirical canonical correlation analysis in subspaces [PDF]
Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004., 2005 This paper addresses canonical correlation analysis of two-channel data, when channel covariances are estimated from a limited number of samples, and are not necessarily full-rank. We show that empirical canonical correlations measure the cosines of the principal angles between the row spaces of the data matrices for the two channels.
A. Pezeshki +3 more
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Interpretive JIVE: Connections with CCA and an application to brain connectivity
Frontiers in Neuroscience, 2022 Joint and Individual Variation Explained (JIVE) is a model that decomposes multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise.
Raphiel J. Murden +3 more
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Slow feature subspace: A video representation based on slow feature analysis for action recognition
Machine Learning with Applications, 2023 This paper proposes a new video representation for subspace-based action recognition. Traditional subspace-based methods represent a video as a subspace by applying principal component analysis (PCA) to its frames. However, this subspace might lead to an
Suzana Rita Alves Beleza +3 more
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J-Self-Adjoint Projections in Krein Spaces
Journal of Function Spaces, 2020 Let ℋ be a Krein space with fundamental symmetry J. Starting with a canonical block-operator matrix representation of J, we study the regular subspaces of ℋ.
Xiao-Ming Xu, Yile Zhao
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Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning
Machines, 2022 Online monitors of the running gears systems of high-speed trains play critical roles in ensuring operational safety and reliability. Status signals collected from high-speed train running gears are very complex regarding working environments, random ...
Hong Zheng +3 more
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