A1 Journal article (refereed)
Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine? (2020)


Linja, J., Hämäläinen, J., Nieminen, P., & Kärkkäinen, T. (2020). Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?. Machine Learning and Knowledge Extraction, 2(4), 533-557. https://doi.org/10.3390/make2040029


JYU authors or editors


Publication details

All authors or editorsLinja, Joakim; Hämäläinen, Joonas; Nieminen, Paavo; Kärkkäinen, Tommi

Journal or seriesMachine Learning and Knowledge Extraction

eISSN2504-4990

Publication year2020

Publication date13/11/2020

Volume2

Issue number4

Pages range533-557

PublisherMDPI AG

Publication countrySwitzerland

Publication languageEnglish

DOIhttps://doi.org/10.3390/make2040029

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/72650


Abstract

Minimal 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.


Keywordsmachine learningregression analysisalgorithmsapproximationprojection (modelling)

Free keywordsmachine learning; supervised learning; distance–based regression; minimal learning machine; approximate algorithms; ordinary least–squares; singular value decomposition; random projection


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Ministry reportingYes

Reporting Year2020

JUFO rating1


Last updated on 2024-22-04 at 13:58