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 realworld
datasets show that our framework outperforms state-ofthe-
art single-task, multi-task, and transfer learning baselines.