Abstract
This paper presents a novel Bayesian formulation to exploit shared structures across multiple data sources, constructing foundations for effective mining and retrieval across disparate domains. We jointly analyze diverse data sources using a unifying piece of metadata (textual tags). We propose a method based on Bayesian Probabilistic Matrix Factorization (BPMF) which is able to explicitly model the partial knowledge common to the datasets using shared subspaces and the knowledge specific to each dataset using individual subspaces. For the proposed model, we derive an efficient algorithm for learning the joint factorization based on Gibbs sampling. The effectiveness of the model is demonstrated by social media retrieval tasks across single and multiple media. The proposed solution is applicable to a wider context, providing a formal framework suitable for exploiting individual as well as mutual knowledge present across heterogeneous data sources of many kinds.
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References
Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval. Addison-Wesley, Reading (1999)
Datta, R., Joshi, D., Li, J., Wang, J.: Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys (CSUR) 40(2), 1–60 (2008)
Gelman, A.: Bayesian data analysis. CRC Press, Boca Raton (2004)
Gu, Q., Zhou, J.: Learning the shared subspace for multi-task clustering and transductive transfer classification. In: ICDM, pp. 159–168 (2009)
Gupta, S.K., Phung, D., Adams, B., Tran, T., Venkatesh, S.: Nonnegative shared subspace learning and its application to social media retrieval. In: SIGKDD, pp. 1169–1178 (2010)
Ji, S., Tang, L., Yu, S., Ye, J.: Extracting shared subspace for multi-label classification. SIGKDD, 381–389 (2008)
Li, X., Snoek, C.G.M., Worring, M.: Learning social tag relevance by neighbor voting. IEEE Transactions on Multimedia 11(7), 1310–1322 (2009)
Mardia, K.V., Bibby, J.M., Kent, J.T.: Multivariate analysis. Academic Press, NY (1979)
Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using markov chain monte carlo. In: ICML, pp. 880–887 (2008)
Si, S., Tao, D., Geng, B.: Bregman divergence based regularization for transfer subspace learning. IEEE Transactions on Knowledge and Data Engineering 22(7), 929–942 (2009)
Sigurbjörnsson, B., Van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: WWW, pp. 327–336 (2008)
Yan, R., Tesic, J., Smith, J.: Model-shared subspace boosting for multi-label classification. SIGKDD, 834–843 (2007)
Yang, Y., Xu, D., Nie, F., Luo, J., Zhuang, Y.: Ranking with local regression and global alignment for cross media retrieval. In: MM, pp. 175–184 (2009)
Yi, Y., Zhuang, Y., Wu, F., Pan, Y.: Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval. IEEE Transactions on Multimedia 10(3), 437–446 (2008)
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Gupta, S.K., Phung, D., Adams, B., Venkatesh, S. (2011). A Bayesian Framework for Learning Shared and Individual Subspaces from Multiple Data Sources. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20841-6_12
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DOI: https://doi.org/10.1007/978-3-642-20841-6_12
Publisher Name: Springer, Berlin, Heidelberg
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