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Efficient Decision Trees for Tensor Regressions
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AI
DOI：10.1080/10618600.2025.2572325.png)
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Abstract
En 中文
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 variables 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 (Yu, Li, and Liu; Sun et al.). 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, including testing robustness to entrywise input tensor noise, are provided on real and synthetic datasets to illustrate the performance of TT. Our implementation is provided at https://github.com/hrluo/TensorDecisionTreeRegressor. Supplementary materials for this article are available online.
Keywords:
Decision tree regressions
Ensemble methods
Scalar-on-tensor regressions
Tensor-on-tensor regressions
Journal
J
IF:
1.8
Papers:
138
Citations:
6.4K
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