Bayesian Joint Additive Factor Models for Multiview Learning
用于多视图学习的贝叶斯联合加性因子模型AI 解读 ↓
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用于多视图学习的贝叶斯联合加性因子模型
- 研究问题
- 面对信噪比不同的多视图特征，如何同时建模视图内外依赖、预测结果，并兼顾可解释性、特征选择和不确定性量化？
- 方法与思路
- 作者提出联合因子回归及区分共享与视图特有成分的加性模型，设计独立和依赖收缩先验，并开发 Gibbs 采样器。
- 材料陈述的贡献
- 摘要称，依赖先验旨在保障成分可识别性，模型支持灵活分布；多组学分娩时间预测应用展示了相对比较方法的性能增益。
- 阅读关注点
- 应核对共享与特有成分的识别约束、收缩先验的影响、特征选择机制，以及预测比较和不确定性量化的评估方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未给出性能数值、识别条件或区间校准证据，增益范围有限。
原文摘要
It is increasingly common to collect data of multiple different types on the same set of samples. Our focus is on studying relationships between such multiview features and responses. A motivating application arises in the context of precision medicine where multi-omics data are collected to correlate with clinical outcomes. It is of interest to infer dependence within and across views while combining multimodal information to improve the prediction of outcomes. The signal-to-noise ratio can vary substantially across views, motivating more nuanced statistical tools beyond standard late and early fusion. This challenge comes with the need to preserve interpretability, select features, and obtain accurate uncertainty quantification. To address these challenges, we introduce two complementary factor regression models. A baseline Joint Factor Regression (\textsc{jfr}) captures combined variation across views via a single factor set, and a more nuanced Joint Additive FActor Regression (\textsc{jafar}) that decomposes variation into shared and view-specific components. For \textsc{jfr}, we use independent cumulative shrinkage process (\textsc{i-cusp}) priors, while for \textsc{jafar} we develop a dependent version (\textsc{d-cusp}) designed to ensure identifiability of the components. We develop Gibbs samplers that exploit the model structure and accommodate flexible feature and outcome distributions. Prediction of time-to-labor onset from immunome, metabolome, and proteome data illustrates performance gains against state-of-the-art competitors. Our open-source software (\texttt{R} package) is available at https://github.com/niccoloanceschi/jafar.