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Spectral clustering based on extended deep ensemble auto encoder with eagle strategy.

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Title: Spectral clustering based on extended deep ensemble auto encoder with eagle strategy.
Authors: Gheytasi, Farshad1 (AUTHOR), Yaghoubyan, S. Hadi1,2 (AUTHOR) yaghoobian.h@gmail.com, Rezaei, Zahra3 (AUTHOR), BagheriFard, Karamollah1,2 (AUTHOR), Parvin, Hamid4,5 (AUTHOR)
Source: Multimedia Tools & Applications. May2024, Vol. 83 Issue 16, p47085-47109. 25p.
Subjects: Computational complexity, Multidimensional databases, Data mapping, Deep learning
Abstract: As an exploratory data aَnalysis (EDA) process, spectral clustering (SC) reduces complex, multidimensional data sets to similar ones in rarer dimensions. Given the big challenges of high computational complexity and lack of accurate mapping in multidimensional data sets, it is essential to provide innovative solutions for SC. Against this background, the present study aims to propose a novel method based on extended Deep learning (DL), recruiting autoencoder (AE) ensembles. Here, the eagle strategy (ES) is further applied, rather than the anchor-based and landmark ones, to eliminate some complexities in SC and fill gaps in data training. As SC has a stochastic optimization structure, the ES may seem appropriate. The Fundamental Clustering and Projection Suite (FCPS) data set correspondingly represents that the proposed method has been able to surpass previous robust algorithms in terms of mean squared error (MSE), accuracy, adjusted random index (ARI), and normalized mutual information (NMI). [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: As an exploratory data aَnalysis (EDA) process, spectral clustering (SC) reduces complex, multidimensional data sets to similar ones in rarer dimensions. Given the big challenges of high computational complexity and lack of accurate mapping in multidimensional data sets, it is essential to provide innovative solutions for SC. Against this background, the present study aims to propose a novel method based on extended Deep learning (DL), recruiting autoencoder (AE) ensembles. Here, the eagle strategy (ES) is further applied, rather than the anchor-based and landmark ones, to eliminate some complexities in SC and fill gaps in data training. As SC has a stochastic optimization structure, the ES may seem appropriate. The Fundamental Clustering and Projection Suite (FCPS) data set correspondingly represents that the proposed method has been able to surpass previous robust algorithms in terms of mean squared error (MSE), accuracy, adjusted random index (ARI), and normalized mutual information (NMI). [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-023-17147-2
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        Text: English
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      – SubjectFull: Data mapping
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      – SubjectFull: Deep learning
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              M: 05
              Text: May2024
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