Tri-SfSVD is a unified sparse functional SVD framework that performs simultaneous subject, feature, and temporal selection for biclustering and triclustering in longitudinal omics and EEG data.
A survey of functional principal component analysis , volume =
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A new MFPCA approach for variable domain data is proposed by running univariate variable-domain FPCA on each variable, stacking the scores, and smoothing the empirical covariance matrix over domain length to recover joint eigenfunctions and scores.
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Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data
Tri-SfSVD is a unified sparse functional SVD framework that performs simultaneous subject, feature, and temporal selection for biclustering and triclustering in longitudinal omics and EEG data.
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Variable Domain Multivariate Functional Principal Component Analysis
A new MFPCA approach for variable domain data is proposed by running univariate variable-domain FPCA on each variable, stacking the scores, and smoothing the empirical covariance matrix over domain length to recover joint eigenfunctions and scores.