A Multi-task Kernel Learning Algorithm for Survival Analysis

Survival analysis aims to predict the occurring times of certain events of interest. Most existing methods for survival analysis either assume specific forms for the underlying stochastic processes or linear hypotheses. To cope with non-linearity in data, we propose a unified framework that combines multi-task and kernel learning for survival analysis. We also develop optimization methods based on the Pegasos (Primal estimated sub-gradient solver for SVM) algorithm for learning. Experiment results demonstrate the effectiveness of the proposed method for survival analysis, on synthetic and real-world data sets.