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Interactive graph construction for graph-based semi-supervised learning

Chen, Changjian, Wang, Zhaowei, Wu, Jing, Wang, Xiting, Guo, Lan-Zhe, Li, Yu-Feng and Liu, Shixia 2021. Interactive graph construction for graph-based semi-supervised learning. IEEE Transactions on Visualization and Computer Graphics 27 (9) , pp. 3701-3716. 10.1109/TVCG.2021.3084694

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Abstract

Semi-supervised learning (SSL) provides a way to improve the performance of prediction models (e.g., classifier) via the usage of unlabeled samples. An effective and widely used method is to construct a graph that describes the relationship between labeled and unlabeled samples. Practical experience indicates that graph quality significantly affects the model performance. In this paper, we present a visual analysis method that interactively constructs a high-quality graph for better model performance. In particular, we propose an interactive graph construction method based on the large margin principle. We have developed a river visualization and a hybrid visualization that combines a scatterplot, a node-link diagram, and a bar chart, to convey the label propagation of graph-based SSL. Based on the understanding of the propagation, a user can select regions of interest to inspect and modify the graph. We conducted two case studies to showcase how our method facilitates the exploitation of labeled and unlabeled samples for improving model performance.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1077-2626
Date of First Compliant Deposit: 18 June 2021
Date of Acceptance: 25 May 2021
Last Modified: 16 Oct 2021 07:19
URI: https://orca.cardiff.ac.uk/id/eprint/141993

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