Shared Structure Learning for Multiple Tasks with Multiple Views
Abstract
Real-world problems usually exhibit dual-heterogeneity, i.e., every task in the problem has features from multiple views, and multiple tasks are related with each other through one or more shared views. To solve these multi-task problems with multiple views, we propose a shared structure learning framework, which can learn shared predictive structures on common views from multiple related tasks, and use the consistency among different views to improve the performance. An alternating optimization algorithm is derived to solve the proposed framework. Moreover, the computation load can be dealt with locally in each task during the optimization, through only sharing some statistics, which significantly reduces the time complexity and space complexity. Experimental studies on four real-world data sets demonstrate that our framework significantly outperforms the state-of-the-art baselines.
Cite
Text
Jin et al. "Shared Structure Learning for Multiple Tasks with Multiple Views." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40991-2_23Markdown
[Jin et al. "Shared Structure Learning for Multiple Tasks with Multiple Views." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/jin2013ecmlpkdd-shared/) doi:10.1007/978-3-642-40991-2_23BibTeX
@inproceedings{jin2013ecmlpkdd-shared,
title = {{Shared Structure Learning for Multiple Tasks with Multiple Views}},
author = {Jin, Xin and Zhuang, Fuzhen and Wang, Shuhui and He, Qing and Shi, Zhongzhi},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {353-368},
doi = {10.1007/978-3-642-40991-2_23},
url = {https://mlanthology.org/ecmlpkdd/2013/jin2013ecmlpkdd-shared/}
}