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dc.contributor.authorYang, Yuanlin
dc.contributor.authorYu, Guoxian
dc.contributor.authorWang, Jun
dc.contributor.authorDomeniconi, Carlotta
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2020-10-13T13:18:21Z
dc.date.available2020-10-13T13:18:21Z
dc.date.issued2020-10-06
dc.identifier.urihttp://hdl.handle.net/10754/665561
dc.description.abstractMulti-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels. M4L is more general and powerful than the typical Multi-view Multi-instance Multi-label Learning (M3L), which only accommodates single-typed bags and lacks the power to jointly model the naturally interconnected multi-typed objects in the physical world. To combat with this novel and challenging learning task, we develop a joint matrix factorization based solution (M4L-JMF). Particularly, M4L-JMF firstly encodes the diverse attributes and multiple inter(intra)-associations among multi-typed bags into respective data matrices, and then jointly factorizes these matrices into low-rank ones to explore the composite latent representation of each bag and its instances (if any). In addition, it incorporates a dispatch and aggregation term to distribute the labels of bags to individual instances and reversely aggregate the labels of instances to their affiliated bags in a coherent manner. Experimental results on benchmark datasets show that M4L-JMF achieves significantly better results than simple adaptions of existing M3L solutions on this novel problem.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2010.02539
dc.rightsArchived with thanks to arXiv
dc.titleMulti-typed Objects Multi-view Multi-instance Multi-label Learning
dc.typePreprint
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.eprint.versionPre-print
dc.contributor.institutionCollege of Computer and Information Sciences, Southwest University, Chongqing, China.
dc.contributor.institutionSchool of Software, Shandong University, Jinan, China.
dc.contributor.institutionJoint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China.
dc.contributor.institutionDepartment of Computer Science, George Mason University, VA, USA.
dc.identifier.arxivid2010.02539
kaust.personYu, Guoxian
kaust.personZhang, Xiangliang
refterms.dateFOA2020-10-13T13:18:54Z
display.summary<p>This record has been merged with an existing record at: <a href="http://hdl.handle.net/10754/667717">http://hdl.handle.net/10754/667717</a>.</p>


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