Abstract
With the rapid development of collection techniques, it is easy to gather various data which come from different domains, such as images, videos, documents, and etc., how to group these heterogeneous data becomes a research issue. Traditional techniques handle these clustering tasks separately, that is one task for one domain, so that they ignore the interactions among domains. In this paper, we present a co-transfer clustering method to deal with these separate tasks together with the aid of co-occurrence data which contain some instances represented in different domains. The proposed method consists of two steps, one is to learn the subspace of different domains which uncovers the latent common topics and respects the intrinsic geometric structure, the next is to simultaneously cluster the instances in all domains via the symmetric nonnegative matrix factorization method. A series of experiments on real-world data sets have shown the performance of the proposed method is better than the state-of-the-art methods.
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References
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: NIPS, pp. 585–592 (2002)
Blaschko, M., Lampert, C.: Correlational spectral clustering. In: CVPR (2008)
Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Wortman, J.: Learning bounds for domain adaptation. In: NIPS (2008)
Caruana, R.: Multitask learning: a knowledge-based source of inductive bias. Mach. Learn. 28, 41–75 (1997)
Chaudhuri, K., Kakade, S., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: ICML, pp. 129–136 (2009)
Chua, T., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: NUS-WIDE: a real-world web image database from National University of Singapore. In: ICICR (2009)
Dai, W., Chen, Y., Xue, G., Yang, Q., Yu, Y.: Translated learning: transfer learning across different feature spaces. In: NIPS, pp. 299–306 (2008)
Ding, C., Li, T., Jordan, M.: Convex and semi-nonnegative matrix factorizations. TPAMI 32(1), 45–55 (2010)
Gartner, T.: A survey of kernels for structured data. KDD 5(1), 49–58 (2003)
Hartigan, J., Wong, M.: A k-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979)
Hotelling, H.: Relations between two sets of variates. Biometrika 28, 321–377 (1936)
Kuang, D., Ding, C., Park, H.: Symmetric nonnegative matrix factorization for graph clustering. In: ICDM, pp. 106–117 (2012)
Lee, D., Seung, H.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)
Lee, D., Seung, H.: Algorithms for non-negative matrix factorization. In: NIPS, pp. 556–562 (2001)
Lu, Z., Zhu, Y., Pan, S., Xiang, E., Wang, Y., Yang, Q.: Source free transfer learning for text classification. In: AAAI (2014)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Ng, M., Wu, Q., Ye, Y.: Co-transfer learning via joint transition probability graph based method. In: KDD Workshop on CDKD, pp. 1–9 (2012)
Ng, M., Wu, Q., Ye, Y.: Co-transfer learning using coupled markov chains with restart. IEEE Intelligent Systems (2013)
Pan, S., Tsang, I., Kwok, J., Yang, Q.: Domain adaptation via transfer component analysis. TNN 22(2), 199–210 (2011)
Pan, S., Yang, Q.: A survey on transfer learning. TKDE 22(10), 1345–1359 (2010)
Qi, G., Aggarwal, C., Huang, T.: Towards semantic knowledge propagation from text corpus to web images. In: WWW, pp. 297–306 (2011)
Shi, J., Malik, J.: Normalized cuts and image segmentation. TPAMI 22(8), 888–905 (2000)
Singh, A., Gordon, G.: Relational learning via collective matrix factorization. In: KDD, pp. 650–658 (2008)
Tan, B., Zhong, E., Ng, M., Yang, Q.: Mixed-transfer: transfer learning over mixed graphs. In: ICDM (2014)
Yang, Q., Chen, Y., Xue, G., Dai, W., Yu, Y.: Heterogeneous transfer learning for image clustering via the social Web. In: ACL/AFNLP, pp. 1–9 (2009)
Zhu, Y., Chen, Y., Lu, Z., Pan, S., Xue, G., Yu, Y., Yang, Q.: Heterogeneous transfer learning for image classification. In: AAAI (2011)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 61375062, Grant 61370129, the Ph.D Programs Foundation of Ministry of Education of China under Grant 20120009110006, the Fundamental Research Funds for the Central Universities under Grant 2014JBM029 and Grant 2014JBZ005, the Program for Changjiang Scholars and Innovative Research Team (IRT 201206), the Planning Project of Science and Technology Department of Hebei Province under Grant 13210347, and the Project of Education Department of Hebei Province under Grant QN20131006.
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Yang, L., Jing, L., Yu, J. (2015). Common Latent Space Identification for Heterogeneous Co-transfer Clustering. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_39
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DOI: https://doi.org/10.1007/978-3-319-23862-3_39
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