针对相异度数据的稳健条件降维
Robust conditional dimension reduction for dissimilarity data
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中文总结 AI 辅助
针对相异度数据中的异常值问题，提出稳健条件多维缩放（rcMDS），用Fair M估计替代平方应力，并开发重加权条件SMACOF算法，实验验证其有效性。
中文摘要 AI 辅助
条件降维（cDR）在学习低维潜在坐标的同时，考虑观测到的协变量，这些协变量代表了数据中已知的变异来源。条件多维缩放（cMDS）是一种直接处理相异度数据的cDR技术。然而，其标准的平方应力公式对受污染的相异度敏感，因为异常值可能主导目标函数并扭曲学习到的配置。我们提出了稳健条件多维缩放（rcMDS），用Fair M估计目标替代平方应力准则。我们开发了一种重加权条件SMACOF算法来优化该目标。所提出的算法具有计算上易处理的更新，其稳定的目标值单调递减并收敛到有限极限。在合成和真实数据上的实验表明，该方
英文摘要
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
发表机构
- Auburn University at Montgomery（奥本大学蒙哥马利分校）
- Virginia Commonwealth University（弗吉尼亚联邦大学）
机构由 AI 辅助整理，请以论文原文为准。
