Graph-Based Semi-supervised Learning for Multi-view Data Analysis

Najmeh Ziraki, Fadi Dornaika (), Alireza Bosaghzadeh () and Nagore Barrena ()
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Najmeh Ziraki: Faculty of Computer Engineering, Shahid Rajaee Teacher Training University
Fadi Dornaika: University of the Basque Country UPV/EHU
Alireza Bosaghzadeh: Faculty of Computer Engineering, Shahid Rajaee Teacher Training University
Nagore Barrena: University of the Basque Country UPV/EHU

Chapter Chapter 10 in Advances in Data Clustering, 2024, pp 181-200 from Springer

Abstract: Abstract This chapter presents an innovative semi-supervised model that aims to improve data analysis by addressing the limitations of relying on single features. Instead, it advocates the simultaneous use of multiple features to improve performance and avoid misleading conclusions. This algorithm focuses on the joint construction of a consistent graph and label estimation. Key highlights of the proposed method include (1) multi-view data representation: the algorithm processes multi-view data, ensuring a richer and more comprehensive representation; (2) semi-supervised classification: by using both labeled and unlabeled data, the algorithm improves classification accuracy and generalization; and (3) improved visualization: in addition to classification, the method also focuses on improving data visualization to provide better insight and understanding. Experimental results on various image databases show the superiority of the fusion approach over single-feature methods or alternative fusion algorithms. These results underline the importance of leveraging multiple features while creating unified graphs to improve performance in semi-supervised data classification and visualization tasks.

Keywords: Multi-view data representation; Semi-supervised classification; Graph fusion; Data visualization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-97-7679-5_10

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DOI: 10.1007/978-981-97-7679-5_10

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