A Graphbased Framework for Multi-Task Multi-View Learning

By Jingrui He, Richard D. Lawrence. 2011

95 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yu Zhang
2. Mohammad Rostami
3. Qiang Yang
4. Xiaotong Zhang
5. Xianchao Zhang
6. Eric Eaton
7. 刘晗
8. Joey Tianyi Zhou

Citations in the corpus

• Transfer Hashing with Privileged Information (2016, background): “Our motivation is from multi-view learning, where the underlying graph structures in different views are assumed to be similar [He and Lawrence, 2011].”
• Cross-Domain Multitask Learning with Latent Probit Models (2012, background): “In particular, (He & Rick, 2011) simultaneously performs multiview learning in each task and multi-task learning in shared views, assuming each task has its own features but may also share features with other tasks.”
• Multi-Task Multi-View Clustering for Non-Negative Data (2015, methodology): “For NG and Email, we adopt the way of constructing multitask multi-view data in [He and Lawrence, 2011; Zhang and Huan, 2012], which has been effectively used to validate the multi-task multi-view learning methods.”
• Learning Transferable Knowledge Through Embedding Spaces (2019, background): “For example, financial decision support agents may have access only to a single data view of tasks or a portion of the non-stationary data distribution [233].”
• Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition (2017, background): “For example, financial decision support agents may have access only to a single data view of tasks or a portion of the non-stationary data distribution [12].”
• Methodologies for Cross-Domain Data Fusion: An Overview (2015, background): “view structural) missing problems, a line of research on multi-view multi-task learning [26], [32] has been proposed.”
• Predicting positive and negative links in signed social networks by transfer learning (2013, methodology): “Some transfer learning methods also use bipartite or tripartite graph as tools to facilitate knowledge transfer [7, 8] by mapping the instances and features to bipartite or tripartite graph nodes.”
• Inductive multi-task learning with multiple view data (2012, background): “[16] proposed a graph-based iterative algorithm (IteM(2)) for multi-view multi-task learning with applications to text classification.”
• Multi-view learning overview: Recent progress and new challenges (2017, background): “He , R. Lawrence , A graph-based framework for multi-task multi-view learning, in: Proceedings of the 28th International Conference on Machine Learn- ing, 2011, pp. 25–32 .”