Integrating Spectral Kernel Learning and Constraints in Semi-Supervised Classification

Authors:
Fanhua Shang;L. C. Jiao;Yuanyuan Liu
Affiliations:
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China 710071;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China 710071;Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China 710071
Venue:
Neural Processing Letters
Year:
2012

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Abstract

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.