On Propagated Scoring for Semisupervised Additive Models
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半监督加性模型中的传播评分
- 研究问题
- 当部分响应有标签、其余响应缺失，同时存在个体特征和图结构信息时，如何统一建模，并处理半监督拟合的退化问题？
- 方法与思路
- 作者将广义加性模型扩展到半监督情形，以固定点正则化的传播评分算法结合特征和图数据，并提出处理退化的正则化策略。
- 材料陈述的贡献
- 摘要称方法使用收缩平滑器，在一定条件下具有一致性，并提供推断与描述工具；还建立与监督模型及能量优化方法的联系。
- 阅读关注点
- 应核对退化出现的条件、图信息与特征信息的结合方式、正则化选择，以及一致性、推断工具和监督比较的具体依据。
- 阅读边界
- 仅据所给 OpenAlex 索引摘要，稿件版本未注明；标签缺失机制和一致性条件未说明，图结构预测不能直接解释为网络因果效应。
原文摘要
This article presents a semisupervised modeling framework that combines feature-based (x) data and graph-based (G) data for classification/regression of the response Y. In this semisupervised setting, Y is observed for a subset of the observations (labeled) and missing for the remainder (unlabeled). The Propagated Scoring algorithm proposed for fitting this model is a semisupervised fixed-point regularization approach that essentially extends the generalized additive model into the semisupervised setting. I first articulate when semisupervised degeneracies are expected within my framework, and then provide a general regularization strategy to address such circumstances. For statistical analysis, I establish that the approach uses shrinking smoothers, provide circumstances in which when the result is consistent, provide measures of inference and description, and establish clear connections to supervised models. Several semisupervised approaches have been considered for the classification problem posed, typically motivated from energy optimization perspective. In this work, I rigorously connect the statistically based propagated scoring framework to this class of approaches. This is particularly insightful, especially with regard to supervised comparisons, because this type of analysis is lacking for the previous work. Two applications are presented, one involving classification of protein location on a cell using a network of protein interaction data and the other involving classification of text documents with citation network information and text data. This article has supplementary material online.