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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.17563 |
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| _version_ | 1866917907002294272 |
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| author | Pfeiffer, Pia Alfons, Andreas Filzmoser, Peter |
| author_facet | Pfeiffer, Pia Alfons, Andreas Filzmoser, Peter |
| contents | Robust statistical estimators offer resilience against outliers but are often computationally challenging, particularly in high-dimensional sparse settings. Modern optimization techniques are utilized for robust sparse association estimators without imposing constraints on the covariance structure. The approach splits the problem into a robust estimation phase, followed by optimization of a decoupled, biconvex problem to derive the sparse canonical vectors. An augmented Lagrangian algorithm, combined with a modified adaptive gradient descent method, induces sparsity through simultaneous updates of both canonical vectors. Results demonstrate improved precision over existing methods, with high-dimensional empirical examples illustrating the effectiveness of this approach. The methodology can also be extended to other robust sparse estimators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_17563 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Efficient Computation of Sparse and Robust Maximum Association Estimators Pfeiffer, Pia Alfons, Andreas Filzmoser, Peter Computation Machine Learning Robust statistical estimators offer resilience against outliers but are often computationally challenging, particularly in high-dimensional sparse settings. Modern optimization techniques are utilized for robust sparse association estimators without imposing constraints on the covariance structure. The approach splits the problem into a robust estimation phase, followed by optimization of a decoupled, biconvex problem to derive the sparse canonical vectors. An augmented Lagrangian algorithm, combined with a modified adaptive gradient descent method, induces sparsity through simultaneous updates of both canonical vectors. Results demonstrate improved precision over existing methods, with high-dimensional empirical examples illustrating the effectiveness of this approach. The methodology can also be extended to other robust sparse estimators. |
| title | Efficient Computation of Sparse and Robust Maximum Association Estimators |
| topic | Computation Machine Learning |
| url | https://arxiv.org/abs/2311.17563 |