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Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine? (2020)

Linja, Joakim; Hämäläinen, Joonas; Nieminen, Paavo; Kärkkäinen, Tommi (2020). Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?. Machine Learning and Knowledge Extraction, 2 (4), 533-557. DOI: 10.3390/make2040029

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Linja, Joakim; Hämäläinen, Joonas; Nieminen, Paavo; Kärkkäinen, Tommi

Lehti tai sarja: Machine Learning and Knowledge Extraction

eISSN: 2504-4990

Julkaisuvuosi: 2020

Volyymi: 2

Lehden numero: 4

Artikkelin sivunumerot: 533-557

Kustantaja: MDPI AG

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

DOI: https://doi.org/10.3390/make2040029

Avoin saatavuus: Open access -julkaisukanavassa ilmestynyt julkaisu

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/72650


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.

YSO-asiasanat: koneoppiminen; regressioanalyysi; algoritmit; approksimointi; projektio

Vapaat asiasanat: machine learning; supervised learning; distance–based regression; minimal learning machine; approximate algorithms; ordinary least–squares; singular value decomposition; random projection

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OKM-raportointi: Kyllä

Alustava JUFO-taso: 1

Viimeisin päivitys 2020-18-11 klo 07:52