Autoencoder in Autoencoder Networks

Modeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed autoencoder in autoencoder networks (AE2-Nets). The pr...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 35; H. 2; S. 2263 - 2275
Hauptverfasser: Zhang, Changqing, Geng, Yu, Han, Zongbo, Liu, Yeqing, Fu, Huazhu, Hu, Qinghua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Bidirectional encoding
Coding
Complementarity
complete representation
Correlation
Data mining
Degradation
Encoding
Feature extraction
Information processing
Kernel
Machine learning
multiview representation learning
Networks
Representation learning
Representations
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:Modeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed autoencoder in autoencoder networks (AE2-Nets). The proposed framework effectively encodes information from high-dimensional heterogeneous data into a compact and informative representation with the proposed bidirectional encoding strategy. Specifically, the proposed AE2-Nets conduct encoding in two directions: the inner-AE-networks extract view-specific intrinsic information (forward encoding), while the outer-AE-networks integrate this view-specific intrinsic information from different views into a latent representation (backward encoding). For the nested architecture, we further provide a probabilistic explanation and extension from hierarchical variational autoencoder. The forward-backward strategy flexibly addresses high-dimensional (noisy) features within each view and encodes complementarity across multiple views in a unified framework. Extensive results on benchmark datasets validate the advantages compared to the state-of-the-art algorithms.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3189239