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Hierarchical One-Class Classifier With Within-Class Scatter-Based Autoencoders.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Aug2021, Vol. 32 Issue 8, p3770-3776. 7p. - Publication Year :
- 2021
-
Abstract
- Autoencoding is a vital branch of representation learning in deep neural networks (DNNs). The extreme learning machine-based autoencoder (ELM-AE) has been recently developed and has gained popularity for its fast learning speed and ease of implementation. However, the ELM-AE uses random hidden node parameters without tuning, which may generate meaningless encoded features. In this brief, we first propose a within-class scatter information constraint-based AE (WSI-AE) that minimizes both the reconstruction error and the within-class scatter of the encoded features. We then build stacked WSI-AEs into a one-class classification (OCC) algorithm based on the hierarchical regularized least-squared method. The effectiveness of our approach was experimentally demonstrated in comparisons with several state-of-the-art AEs and OCC algorithms. The evaluations were performed on several benchmark data sets. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DEEP learning
*ALGORITHMS
*MACHINE learning
*S-matrix theory
*FEATURE extraction
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 32
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 153127742
- Full Text :
- https://doi.org/10.1109/TNNLS.2020.3015860