Journal
NEURAL PROCESSING LETTERS
Volume 36, Issue 2, Pages 101-115Publisher
SPRINGER
DOI: 10.1007/s11063-012-9224-2
Keywords
Semi-supervised classification (SSC); Graph Laplacian; Spectral kernel learning; Mixed knowledge information
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Funding
- National Natural Science Foundation of China [60803097, 61003198, 60970067]
- National Science and Technology Ministry of China [9140A07011810DZ0107, 9140A07021010DZ0131]
- Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [B07048]
- Fundamental Research Funds for the Central Universities [JY10000902001, K50510020001, JY10000902045]
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Recently, integrating new knowledge sources such as pairwise constraints into various classification tasks with insufficient training data has been actively studied in machine learning. In this paper, we propose a novel semi-supervised classification approach, called semi-supervised classification with enhanced spectral kernel, which can simultaneously handle both sparse labeled data and additional pairwise constraints together with unlabeled data. Specifically, we first design a non-parameter spectral kernel learning model based on the squared loss function. Then we develop an efficient semi-supervised classification algorithm which takes advantage of Laplacian spectral regularization: semi-supervised classification with enhanced spectral kernel under the squared loss (ESKS). Finally, we conduct many experiments on a variety of synthetic and real-world data sets to demonstrate the effectiveness of the proposed ESKS algorithm.
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