Abstract
We propose a method called Topic Graph based NMF for Transfer Learning (TNT) based on Non-negative Matrix Factorization (NMF). Since NMF learns feature vectors to approximate the given data, the proposed approach tries to preserve the feature space which is spanned by the feature vectors to realize transfer learning. Based on the learned feature vectors in the source domain, a graph structure called topic graph is constructed, and the graph is utilized as a regularization term in the framework of NMF. We show that the proposed regularization term corresponds to maximizing the similarity between topic graphs in both domains, and that the term corresponds to the graph Laplacian of the topic graph. Furthermore, we propose a learning algorithm with multiplicative update rules and prove its convergence. The proposed method is evaluated over document clustering problem, and the results indicate that the proposed method improves performance via transfer learning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cai, D., He, X., Wu, X., Han, J.: Non-negative matrix factorization on manifold. In: Perner, P. (ed.) ICDM 2008. LNCS (LNAI), vol. 5077, pp. 63–72. Springer, Heidelberg (2008)
Cover, T., Thomas, J.: Elements of Information Theory. Wiley, Chichester (2006)
Dai, W., Xue, G.-R., Yang, Q., Yu, Y.: Co-clustering based classification for out-of-domain documents. In: Proc. of KDD 2007, pp. 210–219 (2007)
Dhillon, J., Modha, D.: Concept decompositions for lage sparse text data using clustering. Machine Learning 42, 143–175 (2001)
Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix tri-factorizations for clustering. In: Proc. of KDD 2006, pp. 126–135 (2006)
Harville, D.A.: Matrix Algebra From a Statistican’s Perspective. Springer, Heidelberg (2008)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proc. of SIGIR 1999, pp. 50–57 (1999)
Kamvar, S.D., Klein, D., Manning, C.D.: Spectral learning. In: Proc. of IJCAI 2003, pp. 561–566 (2003)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Proc. of Neural Information Processing Systems (NIPS), pp. 556–562 (2001)
Ling, X., Dai, W., Xue, G., Yang, Q., Yu, Y.: Spectral domain-transfer learning. In: Proc. of KDD 2008, pp. 488–496 (2008)
Pan, S.J., Yang, Q.: A survey on transfer learning, pp. 1345–1359 (2009)
Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.: Self-taught learning:transfer learning from unlabeled data. In: Proc. of ICML 2007, pp. 759–766 (2007)
von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)
Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: Proc. of SIGIR 2003, pp. 267–273 (2003)
Zhuang, F., Luo, P., Xiaong, H., He, Y., Xiong, Q., Shi, Z.: Exploiting associations between word clusters and document classes for cross-domain text categorization. In: Proc. of ICDM 2010, pp. 13–24 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ogino, H., Yoshida, T. (2011). Topic Graph Based Non-negative Matrix Factorization for Transfer Learning. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., RaÅ›, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-21916-0_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21915-3
Online ISBN: 978-3-642-21916-0
eBook Packages: Computer ScienceComputer Science (R0)