Group COMBSS: Group Selection via Continuous Optimization
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913322570350592 |
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| author | Mathur, Anant Moka, Sarat Liquet, Benoit Botev, Zdravko |
| author_facet | Mathur, Anant Moka, Sarat Liquet, Benoit Botev, Zdravko |
| contents | We present a new optimization method for the group selection problem in linear regression. In this problem, predictors are assumed to have a natural group structure and the goal is to select a small set of groups that best fits the response. The incorporation of group structure in a predictor matrix is a key factor in obtaining better estimators and identifying associations between response and predictors. Such a discrete constrained problem is well-known to be hard, particularly in high-dimensional settings where the number of predictors is much larger than the number of observations. We propose to tackle this problem by framing the underlying discrete binary constrained problem into an unconstrained continuous optimization problem. The performance of our proposed approach is compared to state-of-the-art variable selection strategies on simulated data sets. We illustrate the effectiveness of our approach on a genetic dataset to identify grouping of markers across chromosomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13339 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Group COMBSS: Group Selection via Continuous Optimization Mathur, Anant Moka, Sarat Liquet, Benoit Botev, Zdravko Methodology Computation We present a new optimization method for the group selection problem in linear regression. In this problem, predictors are assumed to have a natural group structure and the goal is to select a small set of groups that best fits the response. The incorporation of group structure in a predictor matrix is a key factor in obtaining better estimators and identifying associations between response and predictors. Such a discrete constrained problem is well-known to be hard, particularly in high-dimensional settings where the number of predictors is much larger than the number of observations. We propose to tackle this problem by framing the underlying discrete binary constrained problem into an unconstrained continuous optimization problem. The performance of our proposed approach is compared to state-of-the-art variable selection strategies on simulated data sets. We illustrate the effectiveness of our approach on a genetic dataset to identify grouping of markers across chromosomes. |
| title | Group COMBSS: Group Selection via Continuous Optimization |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2404.13339 |