Results 11 to 20 of about 1,611 (136)
Generalized Canonical Correlation Analysis: A Subspace Intersection Approach [PDF]
IEEE Transactions on Signal Processing, 2021 Generalized Canonical Correlation Analysis (GCCA) is an important tool that finds numerous applications in data mining, machine learning, and artificial intelligence. It aims at finding `common' random variables that are strongly correlated across multiple feature representations (views) of the same set of entities. CCA and to a lesser extent GCCA have
Mikael Sørensen +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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Approximate Canonical Correlation Analysis for common/specific subspace decompositions [PDF]
Biomedical Signal Processing and Control, 2021 The objective of this paper is to present a new technique for jointly decomposing two sets of signals. The proposed method is a modified version of Canonical Correlation Analysis (CCA), which automatically identifies from the two (a priori noisy) data-sets, having the same number of samples but potentially different number of variables (measurements ...
Ranta, Radu +6 more
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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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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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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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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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A Perturbation Bound on the Subspace Estimator from Canonical Projections
2022 IEEE International Symposium on Information Theory (ISIT), 2022 To appear in Proc.
Karan Srivastava +1 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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