Introduces distributional matrix completion via kernel embeddings and functional unfolding operators, with non-asymptotic error bounds for a novel estimator.
Tensor decom- position meets rkhs: Efficient algorithms for smooth and misaligned data.arXiv preprint arXiv:2408.05677
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A functional tensor model with common invariant subspaces and RKHS-based estimation is introduced for dynamic multilayer networks to handle shared structures, temporal smoothness, and layer heterogeneity.
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Low-rank Distributional Matrix Completion
Introduces distributional matrix completion via kernel embeddings and functional unfolding operators, with non-asymptotic error bounds for a novel estimator.
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A functional tensor model for dynamic multilayer networks with common invariant subspaces and the RKHS estimation
A functional tensor model with common invariant subspaces and RKHS-based estimation is introduced for dynamic multilayer networks to handle shared structures, temporal smoothness, and layer heterogeneity.