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An Auto-Encoder with Genetic Algorithm for High Dimensional Data: Towards Accurate and Interpretable Outlier Detection

When dealing with high-dimensional data, such as in biometric, e-commerce, or industrial applications, it is extremely hard to capture the abnormalities in full space due to the curse of dimensionality. Furthermore, it is becoming increasingly complicated but essential to provide interpretations for...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Jiamu Li, Ji Zhang, Mohamed Jaward Bah, Jian Wang, Youwen Zhu, Gaoming Yang, Lingling Li, Kexin Zhang
Format: Artigo
Sprache:Inglês
Veröffentlicht: MDPI AG 2022-11-01
Schriftenreihe:Algorithms
Schlagworte:
outlier detection
variational autoencoder
genetic algorithm
abnormal subspace
Online-Zugang:https://www.mdpi.com/1999-4893/15/11/429
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