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

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

Clustering is an important and challenging research topic in many fields. Although various clustering algorithms have been developed in the past, traditional shallow clustering algorithms cannot mine the underlying structural information of the data. Recent advances have shown that deep clustering c...

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Bibliographic Details
Main Author: He Ma
Format: Article
Language:English
Published: AIMS Press 2022-07-01
Series:Mathematical Biosciences and Engineering
Subjects:
clustering
deep learning
data representation
variational autoencoder
network architecture
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2022484?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2022484?viewType=HTML

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