Abstract
In the text literature, a variety of useful kernel methods have been developed by many researchers. However, embedding text data into Euclidean space is the key characteristic of common kernels-based text categorization. In this paper, we focus on representation text vectors as points on Riemann manifold and use kernels to integrate discriminative and generative model. And then, we present diffuse kernel based on Dirichlet Compound Multinomial manifold (DCM manifold) which is a space about Dirichlet Compound Multinomial model combining inverse document frequency and information gain. More specifically, as demonstrated by our experimental results on various real-world text datasets, we show that the kernel based on this DCM manifold is more desirable than Euclidean space for text categorization. And our kernel method provides much better computational accuracy than some current state-of-the-art methods.
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References
Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Jaakkola, T.S., Haussler, D.: Exploiting Generative Models in Discriminative Classifier. In: Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems, Denver, Colorado, USA, vol. 11, pp. 487–493. MIT Press, Cambridge (1999)
Jebara, T., Kondor, R., Howard, A.: Probability Product Kernels. The Journal of Machine Learning Research 5, 819–844 (2004)
Kondor, R., Lafferty, J.: Diffusion Kernels on Graphs and Other Discrete Input Spaces. In: Proceedings of the Nineteenth International Conference on Machine Learning, San Mateo, CA, USA, pp. 315–322. Morgan Kaufmann, San Francisco (2002)
Lafferty, J., Lebanon, G.: Diffusion Kernels on Statistical Manifolds. Journal of Machine Learning Research(JMLR) 6, 129–163 (2005)
Zhang, D., Chen, X., Lee, W.S.: Text Classification with Kernels on the Multinomial Manifold. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2005), Salvador, Brazil, pp. 266–273. ACM Press, New York (2005)
Madsen, R.E., Kauchak, D., Elkan, C.: Modeling Word Burstiness Using the Dirichlet Distribution. In: Proceedings of the 22nd International Conference on Machine Learning, New York, NY, USA, pp. 545–552. Morgan Kaufmann, San Francisco (2005)
Lebanon, G.: Metric Learning for Text Documents. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 497–508 (2006)
Minka, T.: Estimating a Dirichlet Distribution (unpublished Paper, 2003) http://research.microsoft.com/~minka
Chang, C.-C., Lin, C.-J.: LIBSVM: A Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
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Zhou, S., Feng, S., Liu, Y. (2008). Kernel-Based Text Classification on Statistical Manifold. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_52
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DOI: https://doi.org/10.1007/978-3-540-87732-5_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
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