Proposes an online variational Bayesian subspace filtering algorithm that learns time-varying subspaces and transition matrices via ARD priors, with a forward-backward implementation showing improved imputation and prediction on traffic and electricity datasets.
Exploring algorithmic limits of matrix rank minimization under affine constraints,
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Online Variational Bayesian Subspace Filtering with Applications
Proposes an online variational Bayesian subspace filtering algorithm that learns time-varying subspaces and transition matrices via ARD priors, with a forward-backward implementation showing improved imputation and prediction on traffic and electricity datasets.