A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document Analysis
In the midst of the ongoing COVID-19 pandemic, there has been a surge in scientific literature aimed at understanding the virus and its impact. However, it has become challenging for a researcher to deal with thousands of articles published daily. This paper proposes a novel deep-learning architectu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10242111/ |
| _version_ | 1797685347393470464 |
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| author | Mebarka Allaoui Mohammed Lamine Kherfi Oussama Aiadi Samir Brahim Belhaouari |
| author_facet | Mebarka Allaoui Mohammed Lamine Kherfi Oussama Aiadi Samir Brahim Belhaouari |
| author_sort | Mebarka Allaoui |
| collection | DOAJ |
| description | In the midst of the ongoing COVID-19 pandemic, there has been a surge in scientific literature aimed at understanding the virus and its impact. However, it has become challenging for a researcher to deal with thousands of articles published daily. This paper proposes a novel deep-learning architecture to organize a large dataset of COVID-19-related scientific literature and provides a clear overview of the current state of knowledge. The proposed model is developed based on two main bases to ensure robustness and efficiency. In particular, we trained a denoising autoencoder with clean and noisy data to make the model can balance, preserving the underline structure and generalizing the new unseen data. Furthermore, the cornerstone of the proposed architecture lies in training the autoencoder using a two-fold objective function that jointly incorporates the data’s reconstruction and clustering. The advantage behind this combination is to avoid the distortion of the latent space and to improve the model efficiency. Afterward, we use the Latent Dirichlet Allocation (LDA) to analyze the document’s topics. For the sake of computational efficiency, instead of feeding the LDA with the whole dataset of documents, we fed it with the clusters produced in the phase of dimensionality reduction and clustering to count the frequency of topics in each cluster. The model was trained on a large public corpus of COVID-19-related articles and evaluated using a set of evaluation metrics. Experimental results indicate the superiority of our proposed model compared to several recent studies. |
| first_indexed | 2024-03-12T00:43:51Z |
| format | Article |
| id | doaj.art-195fbe2d447a442c8c734b53db7bd87b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T00:43:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-195fbe2d447a442c8c734b53db7bd87b2023-09-14T23:00:33ZengIEEEIEEE Access2169-35362023-01-0111969239693810.1109/ACCESS.2023.331262210242111A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document AnalysisMebarka Allaoui0https://orcid.org/0000-0002-1175-6087Mohammed Lamine Kherfi1Oussama Aiadi2https://orcid.org/0000-0002-4102-1735Samir Brahim Belhaouari3https://orcid.org/0000-0003-2336-0490Department of Computer Science and Information Technologies, University Kasdi Merbah Ouargla (UKMO), Ouargla, AlgeriaNational Higher School of Artificial Intelligence, Algiers, AlgeriaDepartment of Computer Science and Information Technologies, University Kasdi Merbah Ouargla (UKMO), Ouargla, AlgeriaDivision of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarIn the midst of the ongoing COVID-19 pandemic, there has been a surge in scientific literature aimed at understanding the virus and its impact. However, it has become challenging for a researcher to deal with thousands of articles published daily. This paper proposes a novel deep-learning architecture to organize a large dataset of COVID-19-related scientific literature and provides a clear overview of the current state of knowledge. The proposed model is developed based on two main bases to ensure robustness and efficiency. In particular, we trained a denoising autoencoder with clean and noisy data to make the model can balance, preserving the underline structure and generalizing the new unseen data. Furthermore, the cornerstone of the proposed architecture lies in training the autoencoder using a two-fold objective function that jointly incorporates the data’s reconstruction and clustering. The advantage behind this combination is to avoid the distortion of the latent space and to improve the model efficiency. Afterward, we use the Latent Dirichlet Allocation (LDA) to analyze the document’s topics. For the sake of computational efficiency, instead of feeding the LDA with the whole dataset of documents, we fed it with the clusters produced in the phase of dimensionality reduction and clustering to count the frequency of topics in each cluster. The model was trained on a large public corpus of COVID-19-related articles and evaluated using a set of evaluation metrics. Experimental results indicate the superiority of our proposed model compared to several recent studies.https://ieeexplore.ieee.org/document/10242111/ClusteringCOVID-19deep learningdimensionality reductiondocument organizationtopic modeling |
| spellingShingle | Mebarka Allaoui Mohammed Lamine Kherfi Oussama Aiadi Samir Brahim Belhaouari A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document Analysis IEEE Access Clustering COVID-19 deep learning dimensionality reduction document organization topic modeling |
| title | A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document Analysis |
| title_full | A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document Analysis |
| title_fullStr | A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document Analysis |
| title_full_unstemmed | A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document Analysis |
| title_short | A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document Analysis |
| title_sort | novel two fold loss function for data clustering and reconstruction application to document analysis |
| topic | Clustering COVID-19 deep learning dimensionality reduction document organization topic modeling |
| url | https://ieeexplore.ieee.org/document/10242111/ |
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