Anomaly localization for network data streams with graph joint sparse PCA
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Content based social behavior prediction: a multi-task learning approach
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Robust Visual Tracking via Structured Multi-Task Sparse Learning
International Journal of Computer Vision
Learning with infinitely many features
Machine Learning
Efficient online learning for multitask feature selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Lead-lag analysis via sparse co-projection in correlated text streams
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Many real world learning problems can be recast as multi-task learning problems which utilize correlations among different tasks to obtain better generalization performance than learning each task individually. The feature selection problem in multi-task setting has many applications in fields of computer vision, text classification and bio-informatics. Generally, it can be realized by solving a L-1-infinity regularized optimization problem. And the solution automatically yields the joint sparsity among different tasks. However, due to the nonsmooth nature of the L-1-infinity norm, there lacks an efficient training algorithm for solving such problem with general convex loss functions. In this paper, we propose an accelerated gradient method based on an ``optimal'' first order black-box method named after Nesterov and provide the convergence rate for smooth convex loss functions. For nonsmooth convex loss functions, such as hinge loss, our method still has fast convergence rate empirically. Moreover, by exploiting the structure of the L-1-infinity ball, we solve the black-box oracle in Nesterov's method by a simple sorting scheme. Our method is suitable for large-scale multi-task learning problem since it only utilizes the first order information and is very easy to implement. Experimental results show that our method significantly outperforms the most state-of-the-art methods in both convergence speed and learning accuracy.