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Multi-resolution Tensor Learning for Large-Scale Spatial Data
[article]
2018
arXiv
pre-print
High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatial tensor models. MMT leverages the property that spatial data can be viewed at multiple resolutions, which are related by coarsening and finegraining from one resolution to another. Using this property, MMT learns a tensor model by starting from a coarse resolution and iteratively increasing the model complexity. In
arXiv:1802.06825v2
fatcat:npigyaxzqvayrcsyyvqweoxt2i