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...
| 主要な著者: | , , |
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| フォーマット: | Journal article |
| 言語: | English |
| 出版事項: |
Springer Nature
2021
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| 要約: | 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 dimensional data. Our
results show superior performance against LightGBM and Field Aware Factorization
Machines (FFM), two algorithms with proven track records, widely used in largescale prediction. We also develop a variational inference framework for KFT
which enables associating the predictions and forecasts with calibrated uncertainty
estimates on several datasets. |
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