Advertisement

Multiview Clustering in Heterogeneous Environment

  • A. Bharathi
  • S. Anitha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)

Abstract

Integrating multiview cluster is a crucial issue in heterogeneous environment. In multiview clustering, objects from the multiple sources can be limited up to homogeneous environment by sharing the same dimensions. In this paper, novel-based tensor methods are used. They are (1) multiview clustering based on the integration of the Frobenius-norm objective function (MC-FR-OI) and (2) matrix integration in the Frobenius-norm objective function (MC-FR-MI). These frameworks worked by using tensor decomposition. Experimental results demonstrate that proposed methods are effective in multiview data integration. Here, higher-order data are used. The performance by using higher-order data is better when compared with the two-dimensional data.

Keywords

Integrating multiview cluster Frobenius-norm objective function Spectral clustering k-means SVD HOSVD 

References

  1. 1.
    L. Tang, X. Wang, H. Liu, Uncovering groups via heterogeneous interaction analysis Google Scholar
  2. 2.
    X. Liu, S. Ji, W. Glanzel, B. De Moor, Fellow, Multiview clustering via tensor methods. IEEE Trans. Know. Data Eng. 25(5) (2013)Google Scholar
  3. 3.
    M. Ishteva, L.D. Lathauwer, P.-A. Absil, S.V. Huffel, Differential-geometric Newton algorithm for the best rank-(r1; r2; r3) approximation of tensors. Numer. Algorithms 51(2), 179–194 (2009)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    L. Tucker, The extension of factor analysis to three-dimensional matrices. Contrib. Math. Psychol. 109–127 (1964)Google Scholar
  5. 5.
    D. Zhou, C.J.C. Burges, Spectral clustering and transductive learning with multiple views, in Proceedings of 24th International Conference Machine Learning (2007), pp. 1159–1166Google Scholar
  6. 6.
    J.D. Carroll, J.J. Chang, Analysis of individual differences in multidimensional scaling via an n-way generalization of echart-young decomposition. Psychometricka 35, 283–319 (1970)CrossRefMATHGoogle Scholar
  7. 7.
    K. Chaudhuri, S.M. Kakade, K. Livescu, K. Sridharan, Multi-view clustering via canonical correlation analysis, in Proceedings of 26th Annals International Conferences Machine Learning (ICML’09) (2009), pp. 129–136Google Scholar
  8. 8.
    H.G. Ayad, M.S. Kamel, Cumulative voting consensus method for partitions with variable number of clusters. IEEE Trans. Pattern Anal. Mach. Intell. 30(1), 160–173 (2008)CrossRefGoogle Scholar
  9. 9.
    T.M. Selee, T.G. Kolda, W.P. Kegelmeyer, J.D. Griffin, Extracting clusters from large datasets with multiple similarity measures using IMSCAND. Summer Proc. (2007)Google Scholar
  10. 10.
    S. Bickel, T. Scheffer, Multi-view clustering, in Proceedings of IEEE Fourth International Conferences Data Mining (ICDM04) (2004), pp. 19–26Google Scholar
  11. 11.
    A. Cichocki, R. Zdunek, A.-H. Phan, S. Amari, Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation (Wiley, Hoboken, 2009)CrossRefGoogle Scholar
  12. 12.
    L. De Lathauwer, B.D. Moor, J. Vandewalle, Multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    L. De Lathauwer, B.D. Moor, J. Vandewalle, On the best rank-1 and rank approximation of higher-order tensors. SIAM J. Matrix Anal. Appl. 21(4), 1324–1342 (2000)CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    A. Ng, M. Jordan, Y. Weiss, On spectral clustering: analysis and an algorithm. Adv. Neural Inf. Process. Syst. 2, 849–856 (2001)Google Scholar
  15. 15.
    J. Sun, D. Tao, C. Faloutsos, Beyond streams and graphs: dynamic tensor analysis, in Proceedings of 12th ACM SIGKDD International Conferences Knowledge Discovery and Data Mining (2006), pp. 374–383Google Scholar
  16. 16.
    J. Ye, Generalized low rank approximations of matrices. Mach. Learn. 61, 167–191 (2005)CrossRefMATHGoogle Scholar
  17. 17.
    U. Luxburg, A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)CrossRefMathSciNetGoogle Scholar
  18. 18.
    B. Long, P.S. Yu, Z.M. Zhang, A general model for multiple view unsupervised learning, in Proceedings of SIAM International Conference Data Mining (2008), pp. 822–833Google Scholar
  19. 19.
    A. Smilde, R. Bro, P. Geladi, in Multi-Way Analysis: Applications in the Chemical Sciences (2004)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Information TechnologySathyamangalam, ErodeIndia

Personalised recommendations