CoxNTF: A New Approach for Joint Clustering and Prediction in Survival Analysis

TL;DR

CoxNTF introduces a non-negative tensor factorization method that enhances survival prediction and clustering by integrating survival information into latent covariate representations, improving interpretability and handling feature redundancy.

Contribution

This paper presents CoxNTF, a novel tensor factorization approach that incorporates survival data into latent covariate representations for improved joint clustering and prediction.

Findings

Achieves survival prediction comparable to Coxnet using original covariates.

Provides interpretable clustering of covariates.

Effectively handles feature redundancy.

Abstract

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.