Sparse minimal learning machine using a diversity measure minimization
Dias, M. L. D., Sousa, L. S., Rocha Neto, A. R. D., Mattos, C. L. C., Gomes, J. P.P., & Kärkkäinen, T. (2019). Sparse minimal learning machine using a diversity measure minimization. In ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 269-274). ESANN. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-178.pdf
Authors
Date
2019Copyright
© The Authors, 2019
The minimal learning machine (MLM) training procedure consists in solving a linear system with multiple measurement vectors (MMV) created between the geometric con gurations of points in the input and output
spaces. Such geometric con gurations are built upon two matrices created using subsets of input and output points, named reference points (RPs). The present paper considers an extension of the focal underdetermined
system solver (FOCUSS) for MMV linear systems problems with additive noise, named regularized MMV FOCUSS (regularized M-FOCUSS), and evaluates it in the task of selecting input reference points for regression
settings. Experiments were carried out using UCI datasets, where the proposal was able to produce sparser models and achieve competitive performance when compared to the regular strategy of selecting MLM input RPs.
Publisher
ESANNParent publication ISBN
978-2-87587-065-0Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningIs part of publication
ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningKeywords
Original source
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-178.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/32125250
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Related funder(s)
Research Council of FinlandFunding program(s)
Research profiles, AoF; Academy Programme, AoFAdditional information about funding
The authors would like to thank UFC, FUNCAP, and Academy of Finland (grants 311877 and 315550) for supporting their research.License
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