TY - GEN
T1 - Multi-task multi-view learning for heterogeneous tasks
AU - Jin, Xin
AU - Zhuang, Fuzhen
AU - Xiong, Hui
AU - Du, Changying
AU - Luo, Ping
AU - He, Qing
N1 - Publisher Copyright:
Copyright 2014 ACM.
PY - 2014/11/3
Y1 - 2014/11/3
N2 - Multi-task multi-view learning deals with the learning scenarios where multiple tasks are associated with each other through multiple shared feature views. All previous works for this problem assume that the tasks use the same set of class labels. However, in real world there exist quite a few applications where the tasks with several views correspond to different set of class labels. This new learning scenario is called Multi-task Multi-view Learning for Heterogeneous Tasks in this study. Then, we propose a Multi-tAsk MUlti-view Discriminant Analysis (MAMUDA) method to solve this problem. Specifically, this method collaboratively learns the feature transformations for different views in different tasks by exploring the shared task-specific and problem intrinsic structures. Additionally, MAMUDA method is convenient to solve the multi-class classification problems. Finally, the experiments on two real-world problems demonstrate the effectiveness of MAMUDA for heterogeneous tasks.
AB - Multi-task multi-view learning deals with the learning scenarios where multiple tasks are associated with each other through multiple shared feature views. All previous works for this problem assume that the tasks use the same set of class labels. However, in real world there exist quite a few applications where the tasks with several views correspond to different set of class labels. This new learning scenario is called Multi-task Multi-view Learning for Heterogeneous Tasks in this study. Then, we propose a Multi-tAsk MUlti-view Discriminant Analysis (MAMUDA) method to solve this problem. Specifically, this method collaboratively learns the feature transformations for different views in different tasks by exploring the shared task-specific and problem intrinsic structures. Additionally, MAMUDA method is convenient to solve the multi-class classification problems. Finally, the experiments on two real-world problems demonstrate the effectiveness of MAMUDA for heterogeneous tasks.
KW - Discriminant analysis
KW - Heterogeneous tasks
KW - Multi-class classification
KW - Multi-task learning
KW - Multi-view learning
UR - https://www.scopus.com/pages/publications/84937598815
U2 - 10.1145/2661829.2662054
DO - 10.1145/2661829.2662054
M3 - 会议稿件
AN - SCOPUS:84937598815
T3 - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
SP - 441
EP - 450
BT - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
Y2 - 3 November 2014 through 7 November 2014
ER -