Autoencoder in Autoencoder Networks

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Autoencoder in Autoencoder Networks
Title:
Autoencoder in Autoencoder Networks
Journal Title:
IEEE Transactions on Neural Networks and Learning Systems
Publication Date:
15 July 2022
Citation:
Zhang, C., Geng, Y., Han, Z., Liu, Y., Fu, H., & Hu, Q. (2022). Autoencoder in Autoencoder Networks. IEEE Transactions on Neural Networks and Learning Systems, 1–13. https://doi.org/10.1109/tnnls.2022.3189239
Abstract:
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 (AE 2 -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 AE 2 -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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the AISG - AISG Tech Challenge Funding
Grant Reference no. : AISG2-TC-2021-003

This work was supported in part by the National Key Research and Development Program of China under Grant 2019YFB2101900; in part by the National Natural Science Foundation of China under Grant 61976151, Grant 61925602, and Grant 61732011; in part by the Natural Science Foundation of Tianjin under Grant 19JCYBJC15200
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2162-2388
2162-237X
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