Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data

arXiv:2311.04829v2 Announce Type: replace
Abstract: Tucker decomposition is a powerful tensor model to handle multi-aspect data. It demonstrates the low-rank property by decomposing the grid-structured data as interactions between a core tensor and a set of object representations (factors). A fundamental assumption of such decomposition is that there are finite objects in each aspect or mode, corresponding to discrete indexes of data entries. However, real-world data is often not naturally posed in this setting. For example, geographic data is represented …

arxiv bayesian continuous cs.lg data functional stat.ml tensor tucker type