A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document Analysis-Reference-Cited by-同舟云学术

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A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document Analysis

Published:2023 Issue: Volume:11 Page:96923-96938
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Allaoui Mebarka¹^ORCID,Kherfi Mohammed Lamine²,Aiadi Oussama¹^ORCID,Belhaouari Samir Brahim³^ORCID

Affiliation:

1. Department of Computer Science and Information Technologies, University Kasdi Merbah Ouargla (UKMO), Ouargla, Algeria

2. National Higher School of Artificial Intelligence, Algiers, Algeria

3. Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10242111.pdf?arnumber=10242111

Reference44 articles.

1. Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature

2. A database for handwritten text recognition research

3. COVID-19 Kaggle Literature Organization

4. Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization

5. Unsupervised deep embedding for clustering analysis;xie;Proc Int Conf Mach Learn,2016

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