Spectral Survival Analysis

TL;DR

This paper introduces a spectral method for survival analysis that scales efficiently to high-dimensional data and deep models, outperforming traditional methods in predictive accuracy and computational efficiency.

Contribution

It establishes a fundamental link between rank regression and CoxPH, extending spectral methods to survival analysis and deep CoxPH variants.

Findings

Outperforms legacy methods in predictive accuracy

Demonstrates scalability on high-dimensional datasets

Effective for deep CoxPH models

Abstract

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.