Sparse minimal learning machine using a diversity measure minimization
Dias, Madson L. D.; Sousa, Lucas S.; Rocha Neto, Ajalmar R. da; Mattos, César L. C.; Gomes, João P. P.; Kärkkäinen, Tommi (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. ESANN, 269-274. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-178.pdf
© 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 congurations of points in the input and output spaces. Such geometric congurations 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.
Parent publication ISBN978-2-87587-065-0
ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Is part of publicationESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Others, AoF; Academy Programme, AoF
Additional information about fundingThe authors would like to thank UFC, FUNCAP, and Academy of Finland (grants 311877 and 315550) for supporting their research.
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