AI RESEARCH
Robust Subspace-Constrained Quadratic Models for Low-Dimensional Structure Learning
arXiv CS.LG
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ArXi:2605.20300v1 Announce Type: new 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. This generalization enables reliable performance under both heavy-tailed and light-tailed noise, thereby substantially enhancing robustness across diverse data regimes.