2012 SemiSupervisedLearningwithMixed

• (Shang et al., 2012) ⇒ Fanhua Shang, L.C. Jiao, and Fei Wang. (2012). “Semi-supervised Learning with Mixed Knowledge Information.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339646

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Semi-supervised+Learning+with+Mixed+Knowledge+Information
• http://dl.acm.org/citation.cfm?id=2339530.2339646&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; graph laplacian; kernel learning; learning; nuclear norm regularization; pairwise constraints; semi-supervised learning

Abstract

Integrating new knowledge sources into various learning tasks to improve their performance has recently become an interesting topic. In this paper we propose a novel semi-supervised learning (SSL) approach, called semi-supervised learning with Mixed Knowledge Information (SSL-MKI) which can simultaneously handle both sparse labeled data and additional pairwise constraints together with unlabeled data. Specifically, we first construct a unified SSL framework to combine the manifold assumption and the pairwise constraints assumption for classification tasks. Then we present a Modified Fixed Point Continuation (MFPC) algorithm with an eigenvalue thresholding (EVT) operator to learn the enhanced kernel matrix. Finally, we develop a two-stage optimization strategy and provide an efficient SSL approach that takes advantage of Laplacian spectral regularization: semi-supervised learning with Enhanced Spectral Kernel(ESK). Experimental results on a variety of synthetic and real-world datasets demonstrate the effectiveness of the proposed ESK approach.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 SemiSupervisedLearningwithMixed	Fei Wang Fanhua Shang L.C. Jiao			Semi-supervised Learning with Mixed Knowledge Information				10.1145/2339530.2339646		2012