Achieving deep clustering through the use of variational autoencoders and similarity-based loss

DOI10.3934/MBE.2022484OpenAlexW4286728325MaRDI QIDQ2688774

He Ma

Publication date: 5 March 2023

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2022484

zbMATH Keywords

clusteringnetwork architecturedata representationdeep learningvariational autoencoder

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Independent component analysis, a new concept?
• Noncommutative geometry framework and the Feynman’s proof of Maxwell equations
• An Introduction to Variational Autoencoders
• Image Clustering Using Local Discriminant Models and Global Integration

This page was built for publication: Achieving deep clustering through the use of variational autoencoders and similarity-based loss