Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
393530 | Information Sciences | 2014 | 13 Pages |
Abstract
The insufficiency of labeled training data is a major obstacle in automatic image annotation. To tackle this problem, we propose a semi-supervised manifold kernel density estimation (SSMKDE) approach based on a recently proposed manifold KDE method. Our contributions are twofold. First, SSMKDE leverages both labeled and unlabeled samples and formulates all data in a manifold structure, which enables a more accurate label prediction. Second, the relationship between KDE-based methods and graph-based semi-supervised learning (SSL) methods is analyzed, which helps to better understand graph-based SSL methods. Extensive experiments demonstrate the superiority of SSMKDE over existing KDE-based and graph-based SSL methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Ping Ji, Na Zhao, Shijie Hao, Jianguo Jiang,