Online Multi-Task Learning based on K-SVD

This paper develops an efficient online algorithm based on K-SVD for learning multiple consecutive tasks. We first derive a batch multi-task learning method that builds upon the K-SVD algorithm, and then extend the batch algorithm to train models online in a lifelong learning setting. The resulting method has lower computational complexity than other current lifelong learning algorithms while maintaining nearly identical performance. Additionally, the proposed method offers an alternate formulation for lifelong learning that supports both task and feature similarity matrices.