Efficient Decision Trees for Tensor Regressions
<|reference_start|>Efficient Decision Trees for Tensor Regressions: 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.<|reference_end|>
@article{luo2024efficient,
    title={Efficient Decision Trees for Tensor Regressions},
    author={Hengrui Luo, Akira Horiguchi, Li Ma},
    journal={arXiv preprint arXiv:2408.01926},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01926},
    primaryClass={cs.LG stat.ME stat.ML}
}