eSPA plus : Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27120%2F22%3A10250372" target="_blank" >RIV/61989100:27120/22:10250372 - isvavai.cz</a>
Result on the web
<a href="https://direct.mit.edu/neco/article-abstract/34/5/1220/110047/eSPA-Scalable-Entropy-Optimal-Machine-Learning?redirectedFrom=fulltext" target="_blank" >https://direct.mit.edu/neco/article-abstract/34/5/1220/110047/eSPA-Scalable-Entropy-Optimal-Machine-Learning?redirectedFrom=fulltext</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1162/neco_a_01490" target="_blank" >10.1162/neco_a_01490</a>
Alternative languages
Result language
angličtina
Original language name
eSPA plus : Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems
Original language description
Classification problems in the small data regime (with small data statistic T and relatively large feature space dimension D) impose challenges for the common machine learning (ML) and deep learning (DL) tools. The standard learning methods from these areas tend to show a lack of robustness when applied to data sets with significantly fewer data points than dimensions and quickly reach the overfitting bound, thus leading to poor performance beyond the training set. To tackle this issue, we propose eSPA+, a significant extension of the recently formulated entropy-optimal scalable probabilistic approximation algorithm (eSPA). Specifically, we propose to change the order of the optimization steps and replace the most computationally expensive subproblem of eSPA with its closed-form solution. We prove that with these two enhancements, eSPA+ moves from the polynomial to the linear class of complexity scaling algorithms. On several small data learning benchmarks, we show that the eSPA+ algorithm achieves a many-fold speed-up with respect to eSPA and even better performance results when compared to a wide array of ML and DL tools. In particular, we benchmark eSPA+ against the standard eSPA and the main classes of common learning algorithms in the small data regime: various forms of support vector machines, random forests, and long short-term memory algorithms. In all the considered applications, the common learning methods and eSPA are markedly outperformed by eSPA+, which achieves significantly higher prediction accuracy with an orders-of-magnitude lower computational cost.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Computation
ISSN
0899-7667
e-ISSN
1530-888X
Volume of the periodical
34
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
36
Pages from-to
1220-1255
UT code for WoS article
000785003800007
EID of the result in the Scopus database
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