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

DOI10.1007/S10994-021-06063-XOpenAlexW3208413417MaRDI QIDQ2127239

Decebal Constantin Mocanu, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Tim van der Lee, Zahra Atashgahi, Raymond Veldhuis

Publication date: 20 April 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2012.00560

zbMATH Keywords

feature selectiondeep learningsparse autoencoderssparse training

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

QuickSelection ⋮ A brain-inspired algorithm for training highly sparse neural networks ⋮ Unnamed Item

Uses Software

• SciPy
• randomForest
• MNIST
• Keras
• GitHub
• COIL-20
• DeepPINK

Cites Work

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• A topological insight into restricted Boltzmann machines
• Feature extraction. Foundations and applications. Papers from NIPS 2003 workshop on feature extraction, Whistler, BC, Canada, December 11--13, 2003. With CD-ROM.
• Wrappers for feature subset selection
• A survey on semi-supervised feature selection methods
• Extremely randomized trees

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