Robust conditional dimension reduction for dissimilarity data

Abstract: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. We developed a reweighted conditional SMACOF algorithm to optimize this objective. The proposed algorithm admits computationally tractable updates, and its stabilized objective values decrease monotonically and converge to a finite limit. Experiments on synthetic and real data show that the pro

Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC); Methodology (stat.ME)

arXiv:2609.06284 [stat.ML]

(or arXiv:2609.06284v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.06284 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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