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dc.contributor.authorLinja, Joakim
dc.contributor.authorHämäläinen, Joonas
dc.contributor.authorNieminen, Paavo
dc.contributor.authorKärkkäinen, Tommi
dc.date.accessioned2020-11-18T05:52:21Z
dc.date.available2020-11-18T05:52:21Z
dc.date.issued2020
dc.identifier.citationLinja, J., Hämäläinen, J., Nieminen, P., & Kärkkäinen, T. (2020). Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?. <i>Machine Learning and Knowledge Extraction</i>, <i>2</i>(4), 533-557. <a href="https://doi.org/10.3390/make2040029" target="_blank">https://doi.org/10.3390/make2040029</a>
dc.identifier.otherCONVID_47038065
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/72650
dc.description.abstractMinimal Learning Machine (MLM) is a recently popularized supervised learning method, which is composed of distance-regression and multilateration steps. The computational complexity of MLM is dominated by the solution of an ordinary least-squares problem. Several different solvers can be applied to the resulting linear problem. In this paper, a thorough comparison of possible and recently proposed, especially randomized, algorithms is carried out for this problem with a representative set of regression datasets. In addition, we compare MLM with shallow and deep feedforward neural network models and study the effects of the number of observations and the number of features with a special dataset. To our knowledge, this is the first time that both scalability and accuracy of such a distance-regression model are being compared to this extent. We expect our results to be useful on shedding light on the capabilities of MLM and in assessing what solution algorithms can improve the efficiency of MLM. We conclude that (i) randomized solvers are an attractive option when the computing time or resources are limited and (ii) MLM can be used as an out-of-the-box tool especially for high-dimensional problems.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.ispartofseriesMachine Learning and Knowledge Extraction
dc.rightsCC BY 4.0
dc.subject.othermachine learning
dc.subject.othersupervised learning
dc.subject.otherdistance–based regression
dc.subject.otherminimal learning machine
dc.subject.otherapproximate algorithms
dc.subject.otherordinary least–squares
dc.subject.othersingular value decomposition
dc.subject.otherrandom projection
dc.titleDo Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202011186669
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange533-557
dc.relation.issn2504-4990
dc.relation.numberinseries4
dc.relation.volume2
dc.type.versionpublishedVersion
dc.rights.copyright© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber315550
dc.relation.grantnumber311877
dc.subject.ysoprojektio (mallinnus)
dc.subject.ysokoneoppiminen
dc.subject.ysoalgoritmit
dc.subject.ysoregressioanalyysi
dc.subject.ysoapproksimointi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p8992
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p2130
jyx.subject.urihttp://www.yso.fi/onto/yso/p4982
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3390/make2040029
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
jyx.fundingprogramAkatemiaohjelma, SAfi
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramAcademy Programme, AoFen
jyx.fundingprogramResearch profiles, AoFen
jyx.fundinginformationThis work was supported by the Academy of Finland from the projects 311877 (Demo) and 315550 (HNP-AI).
datacite.isSupplementedByLinja, Joakim; Hämäläinen, Joonas; Nieminen, Paavo; Kärkkäinen, Tommi (2020). Au38Q MBTR-K3. V. 11.11.2020. Zenodo. https://dx.doi.org/10.5281/zenodo.4268064.
dc.type.okmA1


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