Multiplicative Sparse Tensor Factorization for Multi-View Multi-Task Learning

doi:10.3233/FAIA230561

	Multiplicative Sparse Tensor Factorization for Multi-View Multi-Task Learning
	Wang, Xinyi1 ; Sun, Lu1 ; Nguyen, Canh Hao 2; Mamitsuka, Hiroshi 2
	2023-09-28
会议录名称	FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS
ISSN	0922-6389
卷号	372
页码	2560-2567
DOI	10.3233/FAIA230561
摘要	Multi-View Multi-Task Learning (MVMTL) aims to make predictions on dual-heterogeneous data. Such data contains features from multiple views, and multiple tasks in the data are related with each other through common views. Existing MVMTL methods usually face two major challenges: 1) to save the predictive information from full-order interactions between views efficiently. 2) to learn a parsimonious and highly interpretable model such that the target is related to the features through a subset of interactions. To deal with the challenges, we propose a novel MVMTL method based on multiplicative sparse tensor factorization. For 1), we represent full-order interactions between views as a tensor, that enables to capture the complex correlations in dual-heterogeneous data by a concise model. For 2), we decompose the interaction tensor into a product of two components: one being shared with all tasks and the other being specific to individual tasks. Moreover, tensor factorization is applied to control the model complexity and learn a consensus latent representation shared by multiple tasks. Theoretical analysis reveals the equivalence between our method and a family of models with a joint but more general form of regularizers. Experiments on both synthetic and real-world datasets prove its effectiveness. © 2023 The Authors.
会议录编者/会议主办者	Amazon Alexa ; APTIV ; et al. ; Hewlett Packard ; IDEAS ; Software Force
关键词	Factorization Learning systems Machine learning Heterogeneous data Learn+ Learning methods Multi-views Multiple tasks Multiple views Multitask learning Predictive information Sparse tensors Tensor factorization
会议名称	26th European Conference on Artificial Intelligence, ECAI 2023
会议地点	Krakow, Poland
会议日期	September 30, 2023 - October 4, 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	IOS Press BV
EI入藏号	20234515035269
EI主题词	Tensors
EISSN	1879-8314
EI分类号	723.4 Artificial Intelligence ; 921 Mathematics ; 921.1 Algebra
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/346434
专题	信息科学与技术学院_PI研究组_孙露组信息科学与技术学院_硕士生
作者单位	1.ShanghaiTech University, China; 2.Kyoto University, Japan
第一作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Wang, Xinyi,Sun, Lu,Nguyen, Canh Hao,et al. Multiplicative Sparse Tensor Factorization for Multi-View Multi-Task Learning[C]//Amazon Alexa, APTIV, et al., Hewlett Packard, IDEAS, Software Force:IOS Press BV,2023:2560-2567.