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      Multi-target regression via non-linear output structure learning

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      Embargo Lift Date: 2023-12-18
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      Author(s)
      Arashloo, Shervin Rahimzadeh
      Kittler, J.
      Date
      2021-12-18
      Source Title
      Neurocomputing
      Print ISSN
      0925-2312
      Electronic ISSN
      1872-8286
      Publisher
      Elsevier BV
      Volume
      492
      Pages
      572 - 580
      Language
      English
      Type
      Article
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      Abstract
      The 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
      Keywords
      Multi-output regression
      Non-linear structure learning
      Vector-valued functions in the reproducing kernel
      Hilbert space (RKHSvv)
      Tikhonov regularisation
      Permalink
      http://hdl.handle.net/11693/111304
      Published Version (Please cite this version)
      https://doi.org/10.1016/j.neucom.2021.12.048
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      • Department of Computer Engineering 1561
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