Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.20216 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916633735331840 |
|---|---|
| author | Kapla, Daniel Bura, Efstathia |
| author_facet | Kapla, Daniel Bura, Efstathia |
| contents | We consider supervised learning (regression/classification) problems with tensor-valued input. We derive multi-linear sufficient reductions for the regression or classification problem by modeling the conditional distribution of the predictors given the response as a member of the quadratic exponential family. We develop estimation procedures of sufficient reductions for both continuous and binary tensor-valued predictors. We prove the consistency and asymptotic normality of the estimated sufficient reduction using manifold theory. For continuous predictors, the estimation algorithm is highly computationally efficient and is also applicable to situations where the dimension of the reduction exceeds the sample size. We demonstrate the superior performance of our approach in simulations and real-world data examples for both continuous and binary tensor-valued predictors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20216 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalized Multi-Linear Models for Sufficient Dimension Reduction on Tensor Valued Predictors Kapla, Daniel Bura, Efstathia Methodology We consider supervised learning (regression/classification) problems with tensor-valued input. We derive multi-linear sufficient reductions for the regression or classification problem by modeling the conditional distribution of the predictors given the response as a member of the quadratic exponential family. We develop estimation procedures of sufficient reductions for both continuous and binary tensor-valued predictors. We prove the consistency and asymptotic normality of the estimated sufficient reduction using manifold theory. For continuous predictors, the estimation algorithm is highly computationally efficient and is also applicable to situations where the dimension of the reduction exceeds the sample size. We demonstrate the superior performance of our approach in simulations and real-world data examples for both continuous and binary tensor-valued predictors. |
| title | Generalized Multi-Linear Models for Sufficient Dimension Reduction on Tensor Valued Predictors |
| topic | Methodology |
| url | https://arxiv.org/abs/2502.20216 |