Results 11 to 20 of about 65,242 (175)
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 Sorensen +2 more
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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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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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Subspace perspective on canonical correlation analysis: Dimension reduction and minimax rates [PDF]
Bernoulli, 2020 Canonical correlation analysis (CCA) is a fundamental statistical tool for exploring the correlation structure between two sets of random variables. In this paper, motivated by recent success of applying CCA to learn low dimensional representations of high dimensional objects, we propose to quantify the estimation loss of CCA by the excess prediction ...
Ma, Zhuang, Li, Xiaodong
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Solution of the reconstruction-of-the-measure problem for canonical invariant subspaces [PDF]
Annali di Matematica Pura ed Applicata (1923 -), 2021 We study the Reconstruction-of-the-Measure Problem (ROMP) for commuting 2-variable weighted shifts $W_{( , )}$, when the initial data are given as the Berger measure of the restriction of $W_{( , )}$ to a canonical invariant subspace, together with the marginal measures for the 0-th row and 0-th column in the weight diagram for $W_{( , )}$.
Raúl E. Curto +2 more
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Enhancement of hopping conductivity by spontaneous fractal ordering of low-energy sites [PDF]
, 2016 Variable-range hopping conductivity has long been understood in terms of a canonical prescription for relating the single-particle density of states to the temperature-dependent conductivity.
Chen, Tianran, Skinner, Brian
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Projective cluster-additive transformation for quantum lattice models
SciPost Physics, 2023 We construct a projection-based cluster-additive transformation that block-diagonalizes wide classes of lattice Hamiltonians $\mathcal{H}=\mathcal{H}_0 +V$.
Max Hörmann, Kai P. Schmidt
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Multi-Modal Subspace Fusion via Cauchy Multi-Set Canonical Correlations [PDF]
IEEE Access, 2020 Multi-set canonical correlation analysis (MCCA) is a famous multi-modal coherent subspace learning method. However, sample-based between-modal and within-modal covariance matrices of MCCA usually deviate from real covariance matrices due to noise information and limited sample size. The deviation will weaken the performance of MCCA, especially in image
Yanmin Zhu +3 more
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Kubo-Martin-Schwinger relation for an interacting mobile impurity
Physical Review Research, 2023 In this work we study the Kubo-Martin-Schwinger (KMS) relation in the Yang-Gaudin model of an interacting mobile impurity. We use the integrability of the model to compute the dynamic injection and ejection Green's functions at finite temperatures.
Oleksandr Gamayun +2 more
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Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees [PDF]
, 2010 Recently there is a line of research work proposing to employ Spectral Clustering (SC) to segment (group){Throughout the paper, we use segmentation, clustering, and grouping, and their verb forms, interchangeably.} high-dimensional structural data such ...
Cheong, Loong-Fah +4 more
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