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: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Dordrecht
Springer Netherlands
03.02.2024
Springer Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 1573-7462, 0269-2821, 1573-7462 |
| Online-Zugang: | Volltext |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 28 |
| Audience | Academic |
| Author | Salehi, Elaheh Sadat Daneshfar, Fatemeh Berahmand, Kamal Li, Yuefeng Xu, Yue |
| Author_xml | – sequence: 1 givenname: Kamal surname: Berahmand fullname: Berahmand, Kamal organization: School of Computer Science, Faculty of Science, Queensland University of Technology (QUT) – sequence: 2 givenname: Fatemeh surname: Daneshfar fullname: Daneshfar, Fatemeh email: f.daneshfar@uok.ac.ir organization: Department of Computer Engineering, University of Kurdistan – sequence: 3 givenname: Elaheh Sadat surname: Salehi fullname: Salehi, Elaheh Sadat organization: Department of Electrical and Computer Engineering, University of Shiraz – sequence: 4 givenname: Yuefeng surname: Li fullname: Li, Yuefeng organization: School of Computer Science, Faculty of Science, Queensland University of Technology (QUT) – sequence: 5 givenname: Yue surname: Xu fullname: Xu, Yue organization: School of Computer Science, Faculty of Science, Queensland University of Technology (QUT) |
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| Keywords | Deep learning Dimensionality reduction Reconstruction loss Autoencoder Bottleneck layer Feature extraction Autoencoder application Unsupervised learning |
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| SubjectTerms | 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 |
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