Principle of learning metrics for exploratory data analysis

Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology

Volumn 37, Issue 2-3, 2004, Pages 177-188

  Kaski, Samuel ^a   Sinkkonen, Janne ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Discriminative clustering; Exploratory data analysis; Fisher information matrix; Hebbian learning metrics; Information metric

Indexed keywords

ALGORITHMS; COST EFFECTIVENESS; HEURISTIC METHODS; MAPS; MATHEMATICAL MODELS; METRIC SYSTEM; NEURAL NETWORKS; PROBABILITY DENSITY FUNCTION; QUALITY CONTROL; VECTORS; VISUALIZATION;

DISCRIMINATIVE CLUSTERING; EXPLORATORY DATA ANALYSIS; FISHER INFORMATION MATRIX; HEBBIAN LEARNING METRICS; INFORMATION METRIC;

DATA REDUCTION;

EID: 3543145375     PISSN: 13875485     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:VLSI.0000027483.39774.f8     Document Type: Conference Paper

References (18)

1. J. Sinkkonen and S. Kaski, "Clustering Based on Conditional Distributions in an Auxiliary Space," Neural Computation, vol. 14, 2002, pp. 217-239.
2. S. Kaski, J. Sinkkonen, and J. Peltonen, "Bankruptcy Analysis with Self-Organizing Maps in Learning Metrics," IEEE Transactions on Neural Networks, vol. 12, 2001, pp. 936-947.
3. T. Hastie, R. Tibshirani, and A. Buja, "Flexible Discriminant and Mixture Models," in Neural Networks and Statistics, J. Kay and D. Titterington (Eds.), Oxford University Press, 1995.
4. D.J. Miller and H.S. Uyar, "A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data," in Advances in Neural Information Processing Systems, M. Mozer, M. Jordan, and T. Petsche (Eds.), Cambridge, MA: MIT Press, vol. 9, 1997, pp. 571-577.
5. S.-I. Amari, Differential-Geometrical Methods in Statistics, New York: Springer, 1990.
6. R.E. Kass and P.W. Vos, Geometrical Foundations of Asymptotic Inference, New York: Wiley, 1997.
7. J.W. Fisher III and J. Principe, "A Methodology for Information Theoretic Feature Extraction," in Proceedings of IJCNN'98, International Joint Conference on Neural Networks, Piscataway, NJ: IEEE Service Center, vol. 3, 1998, pp. 1712-1716.
8. K. Torkkola and W. Campbell, "Mutual Information in Learning Feature Transformations," in Proceedings of the 17th International Conference on Machine Learning, Stanford, CA: Morgan Kaufmann, 2000, pp. 1015-1022.
9. T. Kohonen, Self-Organizing Maps, Berlin: Springer, 1995. (Third, extended edition 2001).
10. S. Kaski, J. Kangas, and T. Kohonen, "Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997," Neural Computing Surveys, vol. 1, 1998, pp. 1-176.
11. S.-I. Amari, "Natural Gradient Works Efficiently in Learning," Neural Computation, vol. 10, 1998, pp. 251-276.
12. K. Kiviluoto and P. Bergius, "Exploring Corporate Bankruptcy with Two-Level Self-Organizing Maps, Decision Technologies for Computational Management Science," in Proceedings of Fifth International Conference on Computational Finance, Boston: Kluwer, 1998, pp. 373-380.
13. S. Kaski, "Convergence of a Stochastic Semisupervised Clustering Algorithm," Technical Report A62, Helsinki University of Technology, Espoo, Finland: Publications in Computer and Information Science, 2000.
14. S. Becker, "Mutual Information Maximization: Models of Cortical Self-Organization," Network: Computation in Neural Systems, vol. 7, 1996, pp. 7-31.
15. T. Hofmann, J. Puzicha, and M.I. Jordan, "Learning from Dyadic Data," in Advances in Neural Information Processing Systems, M.S. Kearns, S.A. Solla, and D.A. Cohn (Eds.), San Mateo, CA: Morgan Kaufmann Publishers, vol. 11, 1998, pp. 466-472.
16. F. Pereira, N. Tishby, and L. Lee, "Distributional Clustering of English Words," in Proceedings of the 30th Annual Meeting of the Association for Computational Linguistics, 1993, pp. 183-190.
17. N. Tishby, F.C. Pereira, and W. Bialek, "The Information Bottleneck Method," in 37th Annual Allerton Conference on Communication, Control, and Computing, Urbana, Illinois, 1999.
18. S. Kullback, Information Theory and Statistics, New York: Wiley, 1959.