research-article

Multi-task Multi-view Learning for Heterogeneous Tasks

Authors:

Qing HeAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 441 - 450

https://doi.org/10.1145/2661829.2662054

Published: 03 November 2014 Publication History

Abstract

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.

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Cited By

Wang JShi MZhang XLi YYuan YYang CYu D(2024)Incremental Data Stream Classification with Adaptive Multi-Task Multi-View LearningBig Data Mining and Analytics10.26599/BDMA.2023.90200067:1(87-106)Online publication date: Mar-2024
https://doi.org/10.26599/BDMA.2023.9020006
Luo DXu HCarin L(2023)Differentiable Hierarchical Optimal Transport for Robust Multi-View LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.322256945:6(7293-7307)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TPAMI.2022.3222569
Zhu YWu XQiang JYuan YLi Y(2023)Representation learning via an integrated autoencoder for unsupervised domain adaptationFrontiers of Computer Science10.1007/s11704-022-1349-517:5Online publication date: 5-Jan-2023
https://doi.org/10.1007/s11704-022-1349-5
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Index Terms

Multi-task Multi-view Learning for Heterogeneous Tasks
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

Copyright © 2014 ACM.

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Published: 03 November 2014

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November 3 - 7, 2014

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Cited By

Wang JShi MZhang XLi YYuan YYang CYu D(2024)Incremental Data Stream Classification with Adaptive Multi-Task Multi-View LearningBig Data Mining and Analytics10.26599/BDMA.2023.90200067:1(87-106)Online publication date: Mar-2024
https://doi.org/10.26599/BDMA.2023.9020006
Luo DXu HCarin L(2023)Differentiable Hierarchical Optimal Transport for Robust Multi-View LearningIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.322256945:6(7293-7307)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TPAMI.2022.3222569
Zhu YWu XQiang JYuan YLi Y(2023)Representation learning via an integrated autoencoder for unsupervised domain adaptationFrontiers of Computer Science10.1007/s11704-022-1349-517:5Online publication date: 5-Jan-2023
https://doi.org/10.1007/s11704-022-1349-5
Zhu YZhou XWu X(2022)Unsupervised Domain Adaptation via Stacked Convolutional AutoencoderApplied Sciences10.3390/app1301048113:1(481)Online publication date: 29-Dec-2022
https://doi.org/10.3390/app13010481
Bheda HThaker CShah S(2022)An Optimized VM Placement Approach to Reduce Energy Consumption in Green Cloud ComputingProceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence10.1145/3484824.3484894(130-135)Online publication date: 13-Jan-2022
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Wei FZhao JShan CYuan Z(2022)Alignment and Multi-Scale Fusion for Visual-Tactile Object Recognition2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892134(1-8)Online publication date: 18-Jul-2022
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Das BPoosarla AMahima VKulkarni C(2022)Learning Conversational Stance from Multiple Views of Social Graph Using Multitask and Other ApproachesProceedings of Second International Conference on Sustainable Expert Systems10.1007/978-981-16-7657-4_31(373-388)Online publication date: 26-Feb-2022
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Sun GCong YZhang YZhao GFu Y(2021)Continual Multiview Task Learning via Deep Matrix FactorizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.297749732:1(139-150)Online publication date: Jan-2021
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Lu RLiu JLian SZuo X(2020)Multi-view representation learning in multi-task sceneNeural Computing and Applications10.1007/s00521-019-04577-z32:14(10403-10422)Online publication date: 1-Jul-2020
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Sun LNguyen CMamitsuka H(2019)Fast and robust multi-view multi-task learning via group sparsityProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367527(3499-3505)Online publication date: 10-Aug-2019
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