The effectiveness of supervised feature selection degrades in low training data scenarios. We propose to alleviate this problem by augmenting per-task feature selection with joint feature selection over multiple tasks. Our algorithm builds on the assumption that different tasks have shared structure which could be utilized to cope with data sparsity. The proposed trace-ratio based model not only selects discriminative features for each task, but also finds features which are discriminative over all tasks. Extensive experiment on different data sets demonstrates the effectiveness of our algorithm in low training data scenarios.