Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model

Mortality prediction of rare cancer types with a small number of high-dimensional samples is a challenging task. We propose a transfer learning model where both classes in rare cancers (target task) are modeled in a joint framework by transferring knowledge from the source task. The knowledge transfer is at the data level where only 'related' data points are chosen to train the target task. Moreover, both positive and negative class in training enhances the discrimination power of the proposed framework. Overall, this approach boosts the generalization performance of target task with a small number of data points. The formulation of the proposed framework is convex and expressed as a primal problem. We convert this to a dual problem and efficiently solve by alternating direction multipliers method. Our experiments with both synthetic and three real-world datasets show that our framework outperforms state-of-the-art single-task, multi-task, and transfer learning baselines.