Online learning for multi-task feature selection
Abstract
Multi-task feature selection (MTFS) is an important tool to learn the explanatory features across multiple related tasks. Previous MTFS methods fulfill this task in batch-mode training. This makes them inefficient when data come in sequence or when the number of training data is so large that they cannot be loaded into the memory simultaneously. To tackle these problems, we propose the first online learning framework for MTFS. A main advantage of the online algorithms is the efficiency in both time complexity and memory cost due to the closed-form solutions in updating the model weights at each iteration. Experimental results on a real-world dataset attest to the merits of the proposed algorithms.
Authors: Haiqin Yang, Irwin King, Michael Rung-Tsong Lyu
Published in: ACM International Conference on Information and Knowledge Management (CIKM) (2010)