Large Scale Graph Construction and Label Propagation

Z. Ibrahim, A. Bosaghzadeh, F. Dornaika, F. Dornaika

2024

Abstract

Despite the advances in semi-supervised learning methods, these algorithms face three limitations. The first is the assumption of pre-constructed graphs and the second is their inability to process large databases. The third limitation is that these methods ignore the topological imbalance of the data in a graph. In this paper, we address these limitations and propose a new approach called Weighted Simultaneous Graph Construction and Reduced Flexible Manifold Embedding (W-SGRFME). To overcome the first limitation, we construct the affinity graph using an automatic algorithm within the learning process. The second limitation concerns the ability of the model to handle a large number of unlabeled samples. To this end, the anchors are included in the algorithm as data representatives, and an inductive algorithm is used to estimate the labeling of a large number of unseen samples. To address the topological imbalance of the data samples, we introduced the Renode method to assign weights to the labeled samples. We evaluate the effectiveness of the proposed method through experimental results on two large datasets commonly used in semi-supervised learning: Covtype and MNIST. The results demonstrate the superiority of the W-SGRFME method over two recently proposed models and emphasize its effectiveness in dealing with large datasets.

Paper Citation

in Harvard Style

Ibrahim Z., Bosaghzadeh A. and Dornaika F. (2024). Large Scale Graph Construction and Label Propagation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 702-709. DOI: 10.5220/0012430100003660

in Bibtex Style

@conference{visapp24,
author={Z. Ibrahim and A. Bosaghzadeh and F. Dornaika},
title={Large Scale Graph Construction and Label Propagation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={702-709},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012430100003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Large Scale Graph Construction and Label Propagation
SN - 978-989-758-679-8
AU - Ibrahim Z.
AU - Bosaghzadeh A.
AU - Dornaika F.
PY - 2024
SP - 702
EP - 709
DO - 10.5220/0012430100003660
PB - SciTePress