发表机构
Can Tho University（芹苳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对特征与样本筛选的对偶关系问题，引入Fenchel-Rockafellar表示，证明二者构成等变对，为机器学习降本提供了理论支撑。
AI 中文摘要
特征筛选和样本筛选分别通过去除无关特征和无信息样本来降低机器学习的成本。尽管二者被视为原始-对偶对应物，但其关系仍不正式且依赖于模型。将筛选和对偶性视为目标函数的变换，我们引入了Fenchel-Rockafellar（FR）表示，这是一类包含Lasso和SVM的凸问题，在两种变换下均保持封闭性。随后我们证明，特征筛选和样本筛选构成一个等变对：对偶化后接特征筛选，等价于样本筛选后接对偶化。
英文摘要
Feature and sample screening reduce the cost of machine learning by eliminating irrelevant features and noninformative samples, respectively. Although recognized as primal-dual counterparts, their relationship remains informal and model-dependent. Viewing screening and duality as transformations of objective functions, we introduce Fenchel-Rockafellar (FR) representations, a class of convex problems encompassing the Lasso and SVM that is closed under both transformations. We then prove that feature and sample screening form an equivariant pair: dualization followed by feature screening is equal to sample screening followed by dualization.
