Agglomerative independent variable group analysis

Neurocomputing

Volumn 71, Issue 7-9, 2008, Pages 1311-1320

  Honkela, Antti ^a   Seppä, Jeremias ^a   Alhoniemi, Esa ^b  

^a AALTO UNIVERSITY   (Finland)

^b UNIVERSITY OF TURKU   (Finland)

Author keywords

Hierarchical clustering; Independent variable group analysis; Mutual information; Variable grouping; Variational Bayesian learning

Indexed keywords

CLUSTERING ALGORITHMS; COMPUTER SIMULATION; FEATURE EXTRACTION; HIERARCHICAL SYSTEMS; LEARNING ALGORITHMS;

HIERARCHICAL CLUSTERING; INDEPENDENT VARIABLE GROUP ANALYSIS; VARIATIONAL BAYESIAN LEARNING;

DATA REDUCTION;

AGGLOMERATIVE INDEPENDENT VARIABLE GROUP ANALYSIS; ARTICLE; BAYESIAN LEARNING; CLUSTER ANALYSIS; DATA ANALYSIS; DEPENDENT VARIABLE; INDEPENDENT VARIABLE; LEARNING ALGORITHM; PREDICTION; PRIORITY JOURNAL; REFERENCE VALUE; STATISTICAL ANALYSIS; VARIANCE;

EID: 40649084009     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.11.024     Document Type: Article

References (22)

1. Alhoniemi E., Honkela A., Lagus K., Seppä J., Wagner P., and Valpola H. Compact modeling of data using independent variable group analysis. IEEE Trans. Neural Networks 18 6 (2007) 1762-1776
2. Alhoniemi E., Knuutila T., Johnsson M., Röyhkiö J., and Nevalainen O.S. Data mining in maintenance of electronic component libraries. Proceedings of the IEEE 4th International Conference on Intelligent Systems Design and Applications vol. 1 (2004)
3. Antoniak C.E. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Ann. Stat. 2 6 (1974) 1152-1174
4. Battiti R. Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Networks 5 4 (1994) 537-550
5. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, URL: 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉, 1998.
6. G.V. Dijck, M.M.V. Hulle, Speeding up the wrapper feature subset selection in regression by mutual information relevance and redundancy analysis, in: Proceedings of the International Conference on Artificial Neural Networks (ICANN 2006), Lecture Notes in Computer Science, vol. 4131, Springer, Berlin, 2006, pp. 31-40.
7. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), Wiley, New York
8. Ferguson T.S. A Bayesian analysis of some nonparametric problems. Ann. Stat. 1 2 (1973) 209-230
9. K.A. Heller, Z. Ghahramani, Bayesian hierarchical clustering, in: Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), 2005.
10. Honkela A., Seppä J., and Alhoniemi E. Agglomerative independent variable group analysis. Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), Bruges, Belgium (2007)
11. Kraskov A., Stögbauer H., Andrzejak R.G., and Grassberger P. Hierarchical clustering using mutual information. Europhys. Lett. 70 2 (2005) 278-284
12. Kurihara K., Welling M., and Vlassis N. Accelerated variational Dirichlet process mixtures. In: Schölkopf B., Platt J., and Hoffman T. (Eds). Advances in Neural Information Processing Systems vol. 19 (2007), MIT Press, Cambridge, MA 761-768
13. K. Lagus, E. Alhoniemi, H. Valpola, Independent variable group analysis, in: Proceedings of the International Conference on Artificial Neural Networks-ICANN 2001, Lecture Notes in Computer Science, vol. 2130, Springer, Vienna, Austria, 2001.
14. Mann H.B., and Whitney D.R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18 1 (1947) 50-60
15. Nikkilä J., Honkela A., and Kaski S. Exploring the independence of gene regulatory modules. In: Rousu J., Kaski S., and Ukkonen E. (Eds). Proceedings of the Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology, Tuusula, Finland (2006)
16. Pudil P., Novovičová J., and Kittler J. Floating search methods in feature selection. Pattern Recognition Lett. 15 11 (1994) 1119-1125
17. Sethuram J. A constructive definition of Dirichlet priors. Stat. Sin. 4 (1994) 639-650
18. N. Slonim, G.S. Atwal, G. Tkačik, W. Bialek, Estimating mutual information and multi-information in large networks 〈http://arxiv.org/abs/cs/0502017〉, 2005.
19. Slonim N., Atwal G.S., Tkačik G., and Bialek W. Information-based clustering. Proc. Natl. Acad. Sci. 102 51 (2005) 18297-18302
20. Studený M., and Vejnarová J. The multiinformation function as a tool for measuring stochastic dependence. In: Jordan M. (Ed). Learning in Graphical Models (1999), The MIT Press, Cambridge, MA, USA 261-297
21. Zaki M.J., Parthasarathy S., Ogihara M., and Li W. New algorithms for fast discovery of association rules. Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD) (1997)
22. Zhang N.L. Hierarchical latent class models for cluster analysis. J. Mach. Learn. Res. 5 (2004) 697-723