Robust Subspace-Constrained Quadratic Models for Low-Dimensional Structure Learning
The paper presents a robust subspace-constrained quadratic model for learning low-dimensional structures from high-dimensional data. It enhances the existing framework to handle various noise distributions, improving robustness and reconstruction accuracy. The authors also introduce a gradient-based algorithm for efficient optimization and provide a sensitivity analysis of different loss functions.
- ▪The proposed model accommodates a broad class of noise distributions, including generalized Gaussian and radial Laplace models.
- ▪A gradient-based algorithm with a backtracking line-search strategy is developed for stable and efficient convergence.
- ▪Extensive numerical experiments show that the proposed approach outperforms existing methods in terms of robustness and reconstruction accuracy.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20300 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | exDx-hnpadB6 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2605.20300 (cs) [Submitted on 19 May 2026] Title:Robust Subspace-Constrained Quadratic Models for Low-Dimensional Structure Learning Authors:Zheng Zhai, Xiaohui Li View a PDF of the paper titled Robust Subspace-Constrained Quadratic Models for Low-Dimensional Structure Learning, by Zheng Zhai and Xiaohui Li View PDF HTML (experimental) Abstract:In this paper, we propose a robust subspace-constrained quadratic model (SCQM) for learning low-dimensional structure from high-dimensional data. Building upon the subspace-constrained quadratic matrix factorization (SQMF) framework, the proposed model accommodates a broad class of noise distributions, including generalized Gaussian and radial Laplace models.
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