通过自洽一致原则自动加权，有效抑制异常值影响。
SNAP: A Self-Consistent Agreement Principle with Application to Robust Computation
- 基于相互一致性的自监督加权机制，无需标注或先验知识。
- 异常值权重呈指数衰减，高维下仍能忽略其干扰。
- 适用于向量平均与子空间估计，性能超越传统方法。
我们提出SNAP（自洽一致原则），一种基于相互一致性的自监督鲁棒计算框架。根据一致可靠性假设，SNAP为数据项分配权重以量化一致性，强调可信数据并降低异常值影响，无需监督或先验知识。关键结果是异常值权重的指数抑制，即使在高维设置下，异常值对计算的贡献也可忽略不计。我们研究了SNAP加权机制的性质，并展示了其在向量平均和子空间估计中的实际优势。特别地，非迭代SNAP优于迭代Weiszfeld算法及两种多元中位数均值变体。SNAP提供了一种灵活、易用且广泛适用的鲁棒计算方法。
原文摘要 · Abstract (English)
We introduce SNAP (Self-coNsistent Agreement Principle), a self-supervised framework for robust computation based on mutual agreement. Based on an Agreement-Reliability Hypothesis SNAP assigns weights that quantify agreement, emphasizing trustworthy items and downweighting outliers without supervision or prior knowledge. A key result is the Exponential Suppression of Outlier Weights, ensuring that outliers contribute negligibly to computations, even in high-dimensional settings. We study properties of SNAP weighting scheme and show its practical benefits on vector averaging and subspace estimation. Particularly, we demonstrate that non-iterative SNAP outperforms the iterative Weiszfeld algorithm and two variants of multivariate median of means. SNAP thus provides a flexible, easy-to-use, broadly applicable approach to robust computation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。