Seeded random walk for multi-view semi-supervised classification
| Autor: | Zhewen Wang, Kart-Leong Lim, Shiping Wang, Guobao Xiao, Wenzhong Guo |
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| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
Information Systems and Management Exploit Computer science Generalization business.industry Small number Data classification 02 engineering and technology Machine learning computer.software_genre Random walk Management Information Systems Artificial Intelligence Simple (abstract algebra) Robustness (computer science) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Software computer.programming_language |
| Zdroj: | Knowledge-Based Systems. 222:107016 |
| ISSN: | 0950-7051 |
| DOI: | 10.1016/j.knosys.2021.107016 |
| Popis: | Recently, multi-view learning has captured widespread attention in the machine learning area, yet it is still crucial and challenging to exploit beneficial patterns from multi-view data. Specifically, very limited work has been devoted to multi-view semi-supervised learning, where only a small number of labeled data points are available for model training. Therefore, a simple yet efficient seeded random walk scheme is proposed in this paper to address the multi-view semi-supervised classification problem, where known labeled data points serve as random seeds to be walked with certain probability. In this scheme, the semi-supervised classification indicator is obtained based primarily on an arrival probability and a reward matrix, which are computed by leveraging an initial distribution from some random seeds. Besides, theoretical analyses are then provided to indicate a connection of the proposed model with the existing manifold ranking method. Finally, comprehensive experiments on eight publicly available data sets demonstrate the superiority of the proposed model against compared state-of-the-art semi-supervised methods and fully supervised classifiers. Furthermore, experimental results also suggest that the proposed method comes with positive robustness and promising generalization capability in terms of data classification. |
| Databáze: | OpenAIRE |
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