Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders (Extended Abstract)

Zahra Atashgahi*, Ghada A.Z.N. Sokar, Tim van der Lee, Elena Mocanu, Decebal Constantin Mocanu, Raymond N.J. Veldhuis, Mykola Pechenizkiy

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Feature selection, which identifies the most relevant and informative attributes of a dataset has been introduced to address the challenges raised by the emerge of high-dimensional data. Most existing feature selection methods are computationally inefficient; inefficient algorithms lead to high energy consumption, which is not desirable for devices with limited computational and energy resources. In [1], a novel and flexible method for unsupervised feature selection is proposed. This method, named "QuickSelection", introduces the strength of the neuron in sparse neural networks as a criterion to measure the feature importance. When tested on several benchmark datasets, the proposed method is able to achieve the best trade-off of classification and clustering accuracy, running time, and memory usage, among widely used approaches for feature selection.
Original languageEnglish
Title of host publicationBNAIC/BENELEARN 2021
Subtitle of host publicationThe 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning
Number of pages3
Publication statusAccepted/In press - 2021
EventBNAIC/BENELEARN 2021: The 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning - University of Luxembourg, Belval Campus: Faculty of Science, Technology and Medicine, Esch-sur-Alzette, Luxembourg
Duration: 10 Nov 202112 Nov 2021
https://bnaic2021.uni.lu/

Conference

ConferenceBNAIC/BENELEARN 2021
Country/TerritoryLuxembourg
CityEsch-sur-Alzette
Period10/11/2112/11/21
Internet address

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