SOURCE-LINKED INTELLIGENCE

Robust conditional dimension reduction for dissimilarity data

arXiv · AI, language, vision and robotics · article · Sep 5, 2026 · UTC

Conditional dimension reduction (cDR) learns low-dimensional latent coordinates while accounting for observed covariates that represent known sources of variation in the data. Conditional Multidimensional Scaling (cMDS) is a cDR technique that works directly with dissimilarity data. Its standard squared-stress formulation, however, is sensitive to contaminated dissimilarity, since outliers can dominate the objective and distort the learned configuration. We proposed Robust Conditional Multidimensional Scaling (rcMDS) by replacing the squared-stress criterion with a Fair M-estimation objective.

Read original source ↗ Open in workspace

recordType

paper

region

Global