RPCC identifies the support of occluding sparse components in low-rank plus sparse models via variational Bayesian sparse tensor factorization, delivering near-optimal synthetic performance and robust real-world foreground extraction and anomaly detection.
HLRTF: Hierarchi- cal low-rank tensor factorization for inverse problems in multi- dimensional imaging,
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Robust Principal Component Completion
RPCC identifies the support of occluding sparse components in low-rank plus sparse models via variational Bayesian sparse tensor factorization, delivering near-optimal synthetic performance and robust real-world foreground extraction and anomaly detection.