Abstract
Transductive learning is proposed to incorporate both labeled and unlabeled examples into the learning process. Several methods have been developed and show encouraging performance. However, people may meet complicated classification tasks in real world applications, where one category contains multiple components. Traditional transductive learning algorithms are not very effective in such settings. In this paper, we propose a novel transductive learning approach called constrained local regularized transducer(CLRT) for multi-component category classification. CLRT is based on the local separable assumption that it is possible to build a linear predictor in one small area. We implement the assumption by minimizing a unified objective function, which can be optimized globally. Experiment results validate that CLRT can achieve satisfied performance robustly and efficiently.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: ICML, pp. 19–26 (2001)
Chan, P.K., Schlag, M.D.F., Zien, J.Y.: Spectral k-way ratio-cut partitioning and clustering. In: DAC, pp. 749–754 (1993)
Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: KDD, pp. 269–274 (2001)
Evgeniou, T., Pontil, M., Elisseeff, A.: Leave one out error, stability, and generalization of voting combinations of classifiers. Machine Learning, 71–97 (2004)
Gander, W., Golub, G., von Matt, U.: A constrained eigenvalue problem. In: Linear Algebra and its Application, pp. 114/115,815–839 (1989)
Joachims, T.: Transductive inference for text classification using support vector machines. In: ICML, pp. 200–209 (1999)
Joachims, T.: Transductive learning via spectral graph partitioning. In: ICML, pp. 290–297 (2003)
Kleinberg, J.M., Tardos, E.: Approximation algorithms for classification problems with pairwise relationships: Metric labeling and markov random fields. In: FOCS, pp. 14–23 (1999)
Bottou, L., Vapnik, V.: Local learning algorithms. Neural Computation, 888–900 (1992)
Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: WWW, pp. 171–180 (2007)
Pon, R., Cardenas, A., Buttler, D., Critchlow, T.: Tracking multiple topics for finding interesting articles. In: KDD, pp. 560–569 (2007)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
Scholkopf, B., Smola, A.J.: Learning with kernels. MIT Press, Cambridge (2002)
Shi, J., Malik, J.: Normalized cuts and image segmentation. In: CVPR, pp. 731–737 (1997)
Vapnik, V.: Statistical Learning theory. Wiley, Chichester (1998)
Wang, F., Zhang, C.: Regularized clusteirng for documents. In: SIGIR, pp. 95–102 (2007)
Wu, M., Scholkopf, B.: A local learning approach for clustering. In: NIPS, pp. 1529–1536 (2006)
Zhang, T., Oles, F.J.: Text categorization based on regularized linear classification methods. Journal of Information Retrieval 4, 5–31 (2001)
Zhou, D., Bousquet, O., Lal, J.W.T.N., Scholkopf, B.: Learning with local and global consistency. In: NIPS (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, C., Yu, Y. (2008). Constrained Local Regularized Transducer for Multi-Component Category Classification. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_48
Download citation
DOI: https://doi.org/10.1007/978-3-540-89197-0_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89196-3
Online ISBN: 978-3-540-89197-0
eBook Packages: Computer ScienceComputer Science (R0)