Heterogenous Data Fusion via a Probabilistic Latent-Variable Model

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2981)


In a pervasive computing environment, one is facing the problem of handling heterogeneous data from different sources, transmitted over heterogeneous channels and presented on heterogeneous user interfaces. This calls for adaptive data representations keeping as much relevant information as possible while keeping the representation as small as possible. Typically, the gathered data can be high-dimensional vectors with different types of attributes, e.g. continuous, binary and categorical data. In this paper we present – as a first step – a probabilistic latent-variable model, which is capable of fusing high-dimensional heterogenous data into a unified low-dimensional continuous space, and thus brings great benefits for multivariate data analysis, visualization and dimensionality reduction. We adopt a variational approximation to the likelihood of observed data and describe an EM algorithm to fit the model. The advantages of the proposed model are illustrated on toy data and used on real-world painting image data for both visualization and recommendation.


Recommender System Neural Information Processing System Latent Variable Model Mixed Data Binary Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bishop, C.M., Svensen, M., Williams, C.K.: GTM: The generative topographic mapping. Neural Compuation 10(1), 215–234 (1998)CrossRefGoogle Scholar
  2. 2.
    Cohn, D.: Informed projections. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, vol. 15, MIT Press, Cambridge (2003)Google Scholar
  3. 3.
    Collins, M., Dasgupta, S., Schapire, R.: A generalization of principal component analysis to the exponential family. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems, vol. 13, MIT Press, Cambridge (2001)Google Scholar
  4. 4.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Satatistical Learning. Springer, Heidelberg (2001)Google Scholar
  5. 5.
    Jaakkola, T., Jordan, M.: Bayesian parameter estimation via variational methods. Statistics and Computing, 25–37 (2000)Google Scholar
  6. 6.
    Moustaki, I.: A latent trait and a latent class model for mixed observed variables. British Journal of Mathematical and Statistical Psychology 49, 313–334 (1996)zbMATHGoogle Scholar
  7. 7.
    Roweis, S., Ghahramani, Z.: A unifying review of linear gaussian models. Neural Computaion 11, 305–345 (1999)CrossRefGoogle Scholar
  8. 8.
    Sammel, M.D., Ryan, L.M., Legler, J.M.: Latent variable models for mixed discrete and continuous outcomes. Journal of the Royal Statistical Society Series B 59, 667–678 (1997)zbMATHCrossRefGoogle Scholar
  9. 9.
    Tipping, M.E.: Probabilistic visualization of high-dimensional binary data. In: Kearns, M.S., Solla, S.A., Cohn, D.A. (eds.) Advances in Neural Information Processing Systems, vol. 11, pp. 592–598. MIT Press, Cambridge (1999)Google Scholar
  10. 10.
    Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statisitical Scoiety B(61), 611–622 (1999)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  1. 1.Information and CommunicationsSiemens Corporate TechnologyMunichGermany

Personalised recommendations