Elastic deep autoencoder for text embedding clustering by an improved graph regularization
Text clustering is a task for grouping extracted information of the text in different clusters, which has many applications in recommender systems, sentiment analysis, and more. Deep learning-based methods have become increasingly popular due to their high accuracy in identifying nonlinear structure...
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| Vydané v: | Expert systems with applications Ročník 238; s. 121780 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.03.2024
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | Text clustering is a task for grouping extracted information of the text in different clusters, which has many applications in recommender systems, sentiment analysis, and more. Deep learning-based methods have become increasingly popular due to their high accuracy in identifying nonlinear structures. They usually consist of two major parts: dimensionality reduction and clustering. Autoencoders are simple unsupervised neural networks used for better representation of low-dimensional data and have shown good performance in dealing with non-linear features. However, while they utilize the Frobenius norm to deal well with Gaussian noise, they are sensitive to outlier data and Laplacian noise. In this paper, a deep autoencoder with an adapted elastic loss for text embedding clustering (EDA-TEC) is proposed. The elastic loss is a combination of the Frobenius norm and L2,1-norm to consider both types of noises. Additionally, to maintain the high-dimensional data geometric structure, a modified graph regularization term based on the weighted cosine similarity measure is used. EDA-TEC also improves clustering results by considering the sparsity regularization of the manifold representation data. In this jointly end-to-end deep learning model, better representation and text clustering results are achieved with high accuracy on common datasets compared to existing methods.11https://github.com/safinal/text-embedding-clustering. |
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| AbstractList | Text clustering is a task for grouping extracted information of the text in different clusters, which has many applications in recommender systems, sentiment analysis, and more. Deep learning-based methods have become increasingly popular due to their high accuracy in identifying nonlinear structures. They usually consist of two major parts: dimensionality reduction and clustering. Autoencoders are simple unsupervised neural networks used for better representation of low-dimensional data and have shown good performance in dealing with non-linear features. However, while they utilize the Frobenius norm to deal well with Gaussian noise, they are sensitive to outlier data and Laplacian noise. In this paper, a deep autoencoder with an adapted elastic loss for text embedding clustering (EDA-TEC) is proposed. The elastic loss is a combination of the Frobenius norm and L2,1-norm to consider both types of noises. Additionally, to maintain the high-dimensional data geometric structure, a modified graph regularization term based on the weighted cosine similarity measure is used. EDA-TEC also improves clustering results by considering the sparsity regularization of the manifold representation data. In this jointly end-to-end deep learning model, better representation and text clustering results are achieved with high accuracy on common datasets compared to existing methods.11https://github.com/safinal/text-embedding-clustering. |
| ArticleNumber | 121780 |
| Author | Nafisi, Ali Daneshfar, Fatemeh Soleymanbaigi, Sayvan Yamini, Pedram |
| Author_xml | – sequence: 1 givenname: Fatemeh orcidid: 0000-0003-3150-3527 surname: Daneshfar fullname: Daneshfar, Fatemeh email: f.daneshfar@uok.ac.ir – sequence: 2 givenname: Sayvan surname: Soleymanbaigi fullname: Soleymanbaigi, Sayvan email: s.soleymanbaigi@uok.ac.ir – sequence: 3 givenname: Ali orcidid: 0009-0008-5985-3266 surname: Nafisi fullname: Nafisi, Ali email: alinafisi@uok.ac.ir – sequence: 4 givenname: Pedram orcidid: 0009-0001-1191-9579 surname: Yamini fullname: Yamini, Pedram |
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