AI & ML
Smoothed Least-Absolute Conditional Multidimensional Scaling via Iteratively Reweighted Majorization
arXiv stat.TH0 views
arXiv:2611.14345v1 Announce Type: new Abstract: Conditional multidimensional scaling combines latent coordinates with observed covariates to fit pairwise dissimilarities. We develop a smoothed l1 formulation using the shifted Charbonnier loss. We prove residualwise sharpness of the quadratic loss majorizer, monotonicity, compact containment, and stationarity of noncollapsed accumulation points. A uniform additive bound quantifies approximation to exact least-absolute conditional stress. The experiments use paired contamination, separately calibrated loss parameters, combined-configuration recovery, and direct checks of the theorem's inequalities.
Source：https://arxiv.org/abs/2611.14345PreviousOn the geometry of Gaussian Mean Shift DetectionNextGeneralized Cram\'er-Rao Inequalities Associated with $(h,\tau)$-Divergence and $(h,\tau)$-Exponential Family
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