Large scale tensor regression using kernels and variational inference
We outline an inherent flaw of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this. We coin our methodology Kernel Fried Tensor (KFT) and present it as a large-scale prediction and forecasting tool for high dimen...
| Auteurs principaux: | , , |
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| Format: | Journal article |
| Langue: | English |
| Publié: |
Springer Nature
2021
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