将排序回归的谱方法引入生存分析，提升高维数据下的模型效率与性能。
Spectral Survival Analysis
- 利用谱方法重构生存分析中的排序机制，实现高效计算。
- 在多个高维真实数据集上，预测准确率和计算速度均优于传统方法。
- 适用于深度生存模型等变体，特别适合大规模高维数据场景。
生存分析广泛应用于医疗、商业、生态等多个领域。Cox比例风险（CoxPH）模型作为半参数模型，在文献中极为常见。尽管应用广泛且衍生出众多变体，但在大规模数据和深度架构下扩展仍具挑战，尤其在高维情形下。我们发现秩回归与CoxPH模型之间存在根本联系，从而将排序回归的谱方法适配并拓展至生存分析。该方法具有普适性，可自然推广至多种CoxPH变体，包括深度模型。我们在多个真实世界的高维数据集上验证了该方法的可扩展性；实验表明，其在预测性能和计算效率方面均优于现有方法。
原文摘要 · Abstract (English)
Survival analysis is widely deployed in a diverse set of fields, including healthcare, business, ecology, etc. The Cox Proportional Hazard (CoxPH) model is a semi-parametric model often encountered in the literature. Despite its popularity, wide deployment, and numerous variants, scaling CoxPH to large datasets and deep architectures poses a challenge, especially in the high-dimensional regime. We identify a fundamental connection between rank regression and the CoxPH model: this allows us to adapt and extend the so-called spectral method for rank regression to survival analysis. Our approach is versatile, naturally generalizing to several CoxPH variants, including deep models. We empirically verify our method's scalability on multiple real-world high-dimensional datasets; our method outperforms legacy methods w.r.t. predictive performance and efficiency.
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