Fast and Robust Multi-View Multi-Task Learning via Group Sparsity

Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Multi-view multi-task learning has recently attracted more and more attention due to its dual-heterogeneity, i.e.,each task has heterogeneous features from multiple views, and probably correlates with other tasks via common views.Existing methods usually suffer from three problems: 1) lack the ability to eliminate noisy features, 2) hold a strict assumption on view consistency and 3) ignore the possible existence of task-view outliers.To overcome these limitations, we propose a robust method with joint group-sparsity by decomposing feature parameters into a sum of two components,in which one saves relevant features (for Problem 1) and flexible view consistency (for Problem 2),while the other detects task-view outliers (for Problem 3).With a global convergence property, we develop a fast algorithm to solve the optimization problem in a linear time complexity w.r.t. the number of features and labeled samples.Extensive experiments on various synthetic and real-world datasets demonstrate its effectiveness.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019
Pages3499-3505
Number of pages7
ISBN (Electronic)978-0-9992411-4-1
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Joint Conference on Artificial Intelligence - Venetian Macao Resort Hotel, Macao, China
Duration: 10 Aug 201916 Aug 2019
Conference number: 28
https://ijcai19.org/
http://ijcai19.org/

Conference

ConferenceInternational Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI
CountryChina
CityMacao
Period10/08/201916/08/2019
Internet address

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  • Cite this

    Sun, L., Nguyen, C. H., & Mamitsuka, H. (2019). Fast and Robust Multi-View Multi-Task Learning via Group Sparsity. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019 (pp. 3499-3505) https://doi.org/10.24963/ijcai.2019/485