用生存信息指导因子分解，实现可解释的生存预测与聚类。
CoxNTF: A New Approach for Joint Clustering and Prediction in Survival Analysis
- 基于生存信息加权的非负张量分解，生成与生存相关的新特征。
- 预测性能媲美原始变量的Coxnet模型，且能发现有意义的患者分组。
- 适合需要可解释性与高冗余数据处理的生存分析研究者。
生存分析结果的解读常依赖基线协变量的潜在因子表示。然而，现有方法如非负矩阵分解（NMF）未融合生存信息，限制了其预测能力。本文提出CoxNTF，通过非负张量分解（NTF）构建与生存结局紧密相关的有意义潜在表示。该方法利用Coxnet模型估算的生存概率对协变量张量进行加权，引导分解过程。实验表明，CoxNTF的生存预测性能与使用原始协变量的Coxnet相当，同时提供结构化、可解释的聚类框架。此外，新方法有效处理特征冗余，是生存分析中联合聚类与预测的强大工具。
原文摘要 · Abstract (English)
The interpretation of the results of survival analysis often benefits from latent factor representations of baseline covariates. However, existing methods, such as Nonnegative Matrix Factorization (NMF), do not incorporate survival information, limiting their predictive power. We present CoxNTF, a novel approach that uses non-negative tensor factorization (NTF) to derive meaningful latent representations that are closely associated with survival outcomes. CoxNTF constructs a weighted covariate tensor in which survival probabilities derived from the Coxnet model are used to guide the tensorization process. Our results show that CoxNTF achieves survival prediction performance comparable to using Coxnet with the original covariates, while providing a structured and interpretable clustering framework. In addition, the new approach effectively handles feature redundancy, making it a powerful tool for joint clustering and prediction in survival analysis.
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