Label propagation through minimax paths for scalable semi-supervised learning
SCIE
SCOPUS
- Title
- Label propagation through minimax paths for scalable semi-supervised learning
- Authors
- Kye-Hyeon Kim; Choi, S
- Date Issued
- 2014-08-01
- Publisher
- Elsevier
- Abstract
- Semi-supervised learning (SSL) is attractive for labeling a large amount of data. Motivated from cluster assumption, we present a path-based SSL framework for efficient large-scale SSL, propagating labels through only a few important paths between labeled nodes and unlabeled nodes. From the framework, minimax paths emerge as a minimal set of important paths in a graph, leading us to a novel algorithm, minimax label propagation. With an appropriate stopping criterion, learning time is (1) linear with respect to the number of nodes in a graph and (2) independent of the number of classes. Experimental results show the superiority of our method over existing SSL methods, especially on large-scale data with many classes. (C) 2014 Elsevier B.V. All rights reserved.
- Keywords
- Label propagation; Minimax path; Semi-supervised learning; COLLABORATIVE RECOMMENDATION
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/13696
- DOI
- 10.1016/J.PATREC.2014.02.020
- ISSN
- 0167-8655
- Article Type
- Article
- Citation
- PATTERN RECOGNITION LETTERS, vol. 45, page. 17 - 25, 2014-08-01
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