Spectral Survival Analysis
This work addresses scalability issues in survival analysis for fields like healthcare and business, offering a versatile solution that generalizes to deep models, though it appears incremental as it builds on existing spectral methods.
The paper tackled the challenge of scaling Cox Proportional Hazard models to large, high-dimensional datasets by identifying a connection with rank regression and adapting spectral methods, resulting in improved predictive performance and efficiency on real-world datasets.
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.