Deep embedding clustering based on contractive autoencoder
Clustering large and high-dimensional document data has got a great interest. However, current clustering algorithms lack efficient representation learning. Implementing deep learning techniques in document clustering can strengthen the learning processes. In this work, we simultaneously disentangle...
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| Published in: | Neurocomputing (Amsterdam) Vol. 433; pp. 96 - 107 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
14.04.2021
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| Subjects: | |
| ISSN: | 0925-2312 |
| Online Access: | Get full text |
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| Abstract | Clustering large and high-dimensional document data has got a great interest. However, current clustering algorithms lack efficient representation learning. Implementing deep learning techniques in document clustering can strengthen the learning processes. In this work, we simultaneously disentangle the problem of learned representation by preserving important information from the initial data while pushing the original samples and their augmentations together in one hand. Furthermore, we handle the cluster locality preservation issue by pushing neighboring data points together. To that end, we first introduce Contractive Autoencoders. Then we propose a deep embedding clustering framework based on contractive autoencoder (DECCA) to learn document representations. Furthermore, to grasp relevant document or word features, we append the Frobenius norm as penalty term to the conventional autoencoder framework, which helps the autoencoder to perform better. In this way, the contractive autoencoders apprehend the local manifold structure of the input data and compete with the representations learned by existing methods. Finally, we confirm the supremacy of our proposed algorithm over the state-of-the-art results on six real-world images and text datasets. |
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| AbstractList | Clustering large and high-dimensional document data has got a great interest. However, current clustering algorithms lack efficient representation learning. Implementing deep learning techniques in document clustering can strengthen the learning processes. In this work, we simultaneously disentangle the problem of learned representation by preserving important information from the initial data while pushing the original samples and their augmentations together in one hand. Furthermore, we handle the cluster locality preservation issue by pushing neighboring data points together. To that end, we first introduce Contractive Autoencoders. Then we propose a deep embedding clustering framework based on contractive autoencoder (DECCA) to learn document representations. Furthermore, to grasp relevant document or word features, we append the Frobenius norm as penalty term to the conventional autoencoder framework, which helps the autoencoder to perform better. In this way, the contractive autoencoders apprehend the local manifold structure of the input data and compete with the representations learned by existing methods. Finally, we confirm the supremacy of our proposed algorithm over the state-of-the-art results on six real-world images and text datasets. |
| Author | Liang, Xinyan Diallo, Bassoma Li, Tianrui Zhao, Yimiao Khan, Ghufran Ahmad Hu, Jie |
| Author_xml | – sequence: 1 givenname: Bassoma surname: Diallo fullname: Diallo, Bassoma email: sanediallo2003@yahoo.fr organization: Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China – sequence: 2 givenname: Jie surname: Hu fullname: Hu, Jie email: jiehu@swjtu.edu.cn organization: Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China – sequence: 3 givenname: Tianrui surname: Li fullname: Li, Tianrui email: trli@swjtu.edu.cn organization: Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China – sequence: 4 givenname: Ghufran Ahmad surname: Khan fullname: Khan, Ghufran Ahmad email: ghufraan.alig@gmail.com organization: Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China – sequence: 5 givenname: Xinyan surname: Liang fullname: Liang, Xinyan email: liangxinyan48@163.com organization: Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006, Shanxi, China – sequence: 6 givenname: Yimiao surname: Zhao fullname: Zhao, Yimiao email: Zymiao@my.swjtu.edu.cn organization: Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China |
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| Keywords | Representation learning Contractive autoencoder Deep learning Deep embedding clustering Document clustering |
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