Few-Shot High-Dimensional Feature Selection with Lagrange Programming Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00643476" target="_blank" >RIV/67985807:_____/25:00643476 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/IJCNN64981.2025.11227603" target="_blank" >https://doi.org/10.1109/IJCNN64981.2025.11227603</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN64981.2025.11227603" target="_blank" >10.1109/IJCNN64981.2025.11227603</a>

Result language

angličtina

Original language name

Few-Shot High-Dimensional Feature Selection with Lagrange Programming Neural Networks

Original language description

Few-shot learning and high-dimensional feature selection represent significant challenges in machine learning. In this work, we introduce LPNN-FS, a novel feature selection (FS) technique based on a Lagrange Programming Neural Network (Pk-LPNN) originally developed in the context of compressive sampling. LPNN-FS is a continuous-time recurrent neural network whose dynamics inherently converges to an optimal sparse solution of the LASSO, with its equilibrium point acting as a feature selector. Evaluated in combination with specific downstream classifiers, our model is tested on both synthetic and real-world benchmark datasets characterized by high dimensionality and a limited number of observations. The results demonstrate that LPNN-FS competes with or outperforms state-of-the-art methods such as LASSO, k-best, and sparse PCA in this few-shot, high-dimensional setting. Overall, this study opens new avenues for leveraging compressive sampling-based techniques in challenging feature selection problems.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA25-15490S" target="_blank" >GA25-15490S: LEDNeCo: Low Energy Deep Neurocomputing</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

IJCNN 2025: International Joint Conference on Neural Networks Conference Proceedings

ISBN

979-8-3315-1042-8

ISSN

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e-ISSN

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Number of pages

8

Pages from-to

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Publisher name

IEEE

Place of publication

Piscataway

Event location

Rome

Event date

Jun 30, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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