Hierarchical One-Class Model With Subnetwork for Representation Learning and Outlier Detection

The multilayer one-class classification (OCC) frameworks have gained great traction in research on anomaly and outlier detection. However, most multilayer OCC algorithms suffer from loosely connected feature coding, affecting the ability of generated latent space to properly generate a highly discri...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2023-10, Vol.53 (10), p.6303-6316
Main Authors: Zhang, Wandong, Wu, Q. M. Jonathan, Zhao, W. G. Will, Deng, Haojin, Yang, Yimin
Format: Article
Language:English
Subjects:
Algorithms
Anomaly detection
Classification
Data analysis
Data models
Encoding
Hierarchical network
Machine learning
Moore > Penrose inverse (MPI)
Multilayers
Neural networks
Nonhomogeneous media
one-class classification (OCC)
Outliers (statistics)
Representation learning
Representations
Task analysis
Training
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Summary:The multilayer one-class classification (OCC) frameworks have gained great traction in research on anomaly and outlier detection. However, most multilayer OCC algorithms suffer from loosely connected feature coding, affecting the ability of generated latent space to properly generate a highly discriminative representation between object classes. To alleviate this deficiency, two novel OCC frameworks, namely: 1) OCC structure using the subnetwork neural network (OC-SNN) and 2) maximum correntropy-based OC-SNN (MCOC-SNN), are proposed in this article. The novelties of this article are as follows: 1) the subnetwork is used to build the discriminative latent space; 2) the proposed models are one-step learning networks, instead of stacking feature learning blocks and final classification layer to recognize the input pattern; 3) unlike existing works which utilize mean square error (MSE) to learn low-dimensional features, the MCOC-SNN uses maximum correntropy criterion (MCC) for discriminative feature encoding; and 4) a brand-new OCC dataset, called CO-Mask, is built for this research. Experimental results on the visual classification domain with a varying number of training samples from 6131 to 513 061 demonstrate that the proposed OC-SNN and MCOC-SNN achieve superior performance compared to the existing multilayer OCC models. For reproducibility, the source codes are available at https://github.com/W1AE/OCC .
ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3166349