Efficient Decision Trees for Tensor Regressions
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909680119316480 |
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| author | Luo, Hengrui Horiguchi, Akira Ma, Li |
| author_facet | Luo, Hengrui Horiguchi, Akira Ma, Li |
| contents | We proposed the tensor-input tree (TT) method for scalar-on-tensor and tensor-on-tensor regression problems. We first address scalar-on-tensor problem by proposing scalar-output regression tree models whose input variable are tensors (i.e., multi-way arrays). We devised and implemented fast randomized and deterministic algorithms for efficient fitting of scalar-on-tensor trees, making TT competitive against tensor-input GP models. Based on scalar-on-tensor tree models, we extend our method to tensor-on-tensor problems using additive tree ensemble approaches. Theoretical justification and extensive experiments on real and synthetic datasets are provided to illustrate the performance of TT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01926 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Decision Trees for Tensor Regressions Luo, Hengrui Horiguchi, Akira Ma, Li Machine Learning Methodology 62G08, 15A69 G.3 We proposed the tensor-input tree (TT) method for scalar-on-tensor and tensor-on-tensor regression problems. We first address scalar-on-tensor problem by proposing scalar-output regression tree models whose input variable are tensors (i.e., multi-way arrays). We devised and implemented fast randomized and deterministic algorithms for efficient fitting of scalar-on-tensor trees, making TT competitive against tensor-input GP models. Based on scalar-on-tensor tree models, we extend our method to tensor-on-tensor problems using additive tree ensemble approaches. Theoretical justification and extensive experiments on real and synthetic datasets are provided to illustrate the performance of TT. |
| title | Efficient Decision Trees for Tensor Regressions |
| topic | Machine Learning Methodology 62G08, 15A69 G.3 |
| url | https://arxiv.org/abs/2408.01926 |