Gauge-Optimal Approximate Learning for Small Data Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27120%2F24%3A10255906" target="_blank" >RIV/61989100:27120/24:10255906 - isvavai.cz</a>
Result on the web
<a href="https://direct.mit.edu/neco/article-abstract/36/6/1198/120667/Gauge-Optimal-Approximate-Learning-for-Small-Data?redirectedFrom=fulltext" target="_blank" >https://direct.mit.edu/neco/article-abstract/36/6/1198/120667/Gauge-Optimal-Approximate-Learning-for-Small-Data?redirectedFrom=fulltext</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1162/neco_a_01664" target="_blank" >10.1162/neco_a_01664</a>
Alternative languages
Result language
angličtina
Original language name
Gauge-Optimal Approximate Learning for Small Data Classification
Original language description
Small data learning problems are characterized by a significant discrepancy between the limited number of response variable observations and the large feature space dimension. In this setting, the common learning tools struggle to identify the features important for the classification task from those that bear no relevant information and cannot derive an appropriate learning rule that allows discriminating among different classes. As a potential solution to this problem, here we exploit the idea of reducing and rotating the feature space in a lower-dimensional gauge and propose the gauge-optimal approximate learning (GOAL) algorithm, which provides an analytically tractable joint solution to the dimension reduction, feature segmentation, and classification problems for small data learning problems. We prove that the optimal solution of the GOAL algorithm consists in piecewise-linear functions in the Euclidean space and that it can be approximated through a monotonically convergent algorithm that presents-under the assumption of a discrete segmentation of the feature space-a closed-form solution for each optimization substep and an overall linear iteration cost scaling. The GOAL algorithm has been compared to other state-of-the-art machine learning tools on both synthetic data and challenging real-world applications from climate science and bioinformatics (i.e., prediction of the El Ni & ntilde;o Southern Oscillation and inference of epigenetically induced gene-activity networks from limited experimental data). The experimental results show that the proposed algorithm outperforms the reported best competitors for these problems in both learning performance and 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
2024
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
36
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
30
Pages from-to
1198-1227
UT code for WoS article
001268217100003
EID of the result in the Scopus database
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