Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning

Lu Sun, Canh Hao Nguyen, Hiroshi Mamitsuka

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

2 Citations (Scopus)


Multi-view multi-task learning refers to dealing with dual-heterogeneous data,where each sample has multi-view features,and multiple tasks are correlated via common views.Existing methods do not sufficiently address three key challenges:(a) saving task correlation efficiently, (b) building a sparse model and (c) learning view-wise weights.In this paper, we propose a new method to directly handle these challenges based on multiplicative sparse feature decomposition.For (a), the weight matrix is decomposed into two components via low-rank constraint matrix factorization, which saves task correlation by learning a reduced number of model parameters.For (b) and (c), the first component is further decomposed into two sub-components,to select topic-specific features and learn view-wise importance, respectively. Theoretical analysis reveals its equivalence with a general form of joint regularization,and motivates us to develop a fast optimization algorithm in a linear complexity w.r.t. the data size.Extensive experiments on both simulated and real-world datasets validate its efficiency.
Original languageEnglish
Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Number of pages7
ISBN (Electronic)978-0-9992411-4-1
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


ConferenceInternational Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI
Internet address


Dive into the research topics of 'Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning'. Together they form a unique fingerprint.

Cite this