Autoencoders and their applications in machine learning: a survey

Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stim...

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Veröffentlicht in:The Artificial intelligence review Jg. 57; H. 2; S. 28
Hauptverfasser: Berahmand, Kamal, Daneshfar, Fatemeh, Salehi, Elaheh Sadat, Li, Yuefeng, Xu, Yue
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 03.02.2024
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Anomalies
Artificial Intelligence
Classification
Computational linguistics
Computer engineering
Computer Science
Deep learning
Discriminant analysis
Language processing
Machine learning
Machine vision
Natural language interfaces
Natural language processing
Neural networks
Polls & surveys
Principal components analysis
Principles
Probability distribution
Recommender systems
Speech
Taxonomy
Unsupervised learning
Deep learning
Dimensionality reduction
Reconstruction loss
Autoencoder
Bottleneck layer
Feature extraction
Autoencoder application
Unsupervised learning
ISSN:1573-7462, 0269-2821, 1573-7462
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Zusammenfassung:Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stimulating technical improvements and orienting research newbies to autoencoders. In this paper, we present a comprehensive survey of autoencoders, starting with an explanation of the principle of conventional autoencoder and their primary development process. We then provide a taxonomy of autoencoders based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of autoencoders in various fields, including machine vision, natural language processing, complex network, recommender system, speech process, anomaly detection, and others. Lastly, we summarize the limitations of current autoencoder algorithms and discuss the future directions of the field.
Bibliographie:ObjectType-Article-1
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ISSN:1573-7462
0269-2821
1573-7462
DOI:10.1007/s10462-023-10662-6