In Press, Corrected Proof: Multi-target regression via non-linear output structure learning

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buir.contributor.authorArashloo, Shervin Rahimzadeh
buir.contributor.orcidArashloo, Shervin Rahimzadeh|0000-0003-0189-4774
dc.contributor.authorArashloo, Shervin Rahimzadeh
dc.contributor.authorKittler, J.
dc.date.accessioned2022-02-23T08:47:39Z
dc.date.available2022-02-23T08:47:39Z
dc.date.issued2021-12-18
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractThe problem of simultaneously predicting multiple real-valued outputs using a shared set of input variables is known as multi-target regression and has attracted considerable interest in the past couple of years. The dominant approach in the literature for multi-target regression is to capture the dependencies between the outputs through a linear model and express it as an output mixing matrix. This modelling formalism, however, is too simplistic in real-world problems where the output variables are related to one another in a more complex and non-linear fashion. To address this problem, in this study, we propose a structural modelling approach where the correlations between output variables are modelled using a non-linear approach. In particular, we pose the multi-target regression problem as one of vector-valued composition function learning in the reproducing kernel Hilbert space and propose a non-linear structure learning approach to capture the relationship between the outputs via an output kernel. By virtue of using a non-linear output kernel function, the proposed approach can better discover non-linear dependencies among targets for improved prediction performance. An extensive evaluation conducted on different databases reveals the benefits of the proposed multi-target regression technique against the baseline and the state-of-the-art methods.en_US
dc.embargo.release2023-12-18
dc.identifier.doi10.1016/j.neucom.2021.12.048en_US
dc.identifier.eissn1872-8286
dc.identifier.issn0925-2312
dc.identifier.urihttp://hdl.handle.net/11693/77567
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttps://doi.org/10.1016/j.neucom.2021.12.048en_US
dc.source.titleNeurocomputingen_US
dc.subjectMulti-output regressionen_US
dc.subjectNon-linear structure learningen_US
dc.subjectVector-valued functions in the reproducing kernel Hilbert space (RKHSvv)en_US
dc.subjectTikhonov regularisationen_US
dc.titleIn Press, Corrected Proof: Multi-target regression via non-linear output structure learningen_US
dc.typeArticleen_US
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