Clustering based on conditional distributions in an auxiliary space

Neural Computation

Volumn 14, Issue 1, 2002, Pages 217-239

  Sinkkonen, Janne ^a   Kaski, Samuel ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); SUPPORT, NON-U.S. GOV'T;

EID: 0036133934     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976602753284509     Document Type: Article

References (34)

1. Becker, S. (1996). Mutual information maximization: Models of cortical self-organization. Network: Computation in Neural Systems, 7, 7-31.
2. Becker, S., & Hinton, G. E. (1992). Self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, 355, 161-163.
3. Brown, M. P., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares, Jr., M., & Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences, USA, 97, 262-267.
4. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: Wiley.
5. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1-38.
6. Didday, R. L. (1976). A model of visuomotor mechanisms in the frog optic tectum. Mathematical Biosciences, 30, 169-180.
7. Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences, USA, 95, 14863-14868.
8. Fisher III, J. W., & Principe, J. (1998). A methodology for information theoretic feature extraction. In Proc. IJCNN'98, International Joint Conference on Neural Networks (pp. 1712-1716). Piscataway, NJ: IEEE Service Center.
9. Forgy, E. W. (1965). Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications. Biometrics, 21, 768-769.
10. Gersho, A. (1979). Asymptotically optimal block quantization. IEEE Transactions on Information Theory, 25, 373-380.
11. Gray, R. M. (1984, April). Vector quantization. IEEE ASSP Magazine, 4-29.
12. Grossberg, S. (1976). On the development of feature detectors in the visual cortex with applications to learning and reaction-diffusion systems. Biological Cybernetics, 21, 145-159.
13. Hastie, T., Tibshirani, R., & Buja, A. (1995). Flexible discriminant and mixture models. In J. Kay & D. Titterington (Eds.), Neural networks and statistics. New York: Oxford University Press.
14. Hofmann, T. (2000). Learning the similarity of documents: An information-geometric approach to document retrieval and categorization. In S. A. Solla, T. K. Leen, & K.-R. Müller (Eds.), Advances in neural information processing systems, 12 (pp. 914-920). Cambridge, MA: MIT Press.
15. Hofmann, T., Puzicha, J., & Jordan, M. I. (1998). Learning from dyadic data. In M. S. Kearns, S. A. Solla, & D. A. Cohn (Eds.), Advances in mural information processing systems, 11 (pp. 466-472). San Mateo, CA: Morgan Kaufmann.
16. Jaakkola, T. S., & Haussler, D. (1999). Exploiting generative models in discriminative classifiers. In M. S. Kearns, S. A. Solla, & D. A. Cohn (Eds.), Advances in neural information processing systems, 11 (pp. 487-493). San Mateo, CA: Morgan Kaufmann.
17. Kaski, S. (2000). Convergence of a stochastic semisupervised clustering algorithm (Tech. Rep. No. A62). Espoo, Finland: Helsinki University of Technology.
18. Kaski, S., & Kohonen, T. (1994). Winner-take-all networks for physiological models of competitive learning. Neural Networks, 7, 973-984.
19. Kaski, S., Sinkkonen, J., & Peltonen, J. (in press). Bankruptcy analysis with self-organizing maps in learning metrics. IEEE Transactions on Neural Networks.
20. Kohonen, T. (1984). Self-organization and associative memory. Berlin: Springer-Verlag.
21. Kohonen, T. (1993). Physiological interpretation of the self-organizing map algorithm. Neural Networks, 6, 895-905.
22. Kohonen, T. (1995). Self-organizing maps. Berlin: Springer.
23. Kohonen, T., & Hari, R. (1999). Where the abstract feature maps of the brain might come from. TINS, 22, 135-139.
24. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. Le Cam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Vol. 1: Statistics (pp. 281-297). Berkeley: University of California Press.
25. Makhoul, J., Roucos, S., & Gish, H. (1985). Vector quantization in speech coding. Proceedings of the IEEE, 73, 1551-1588.
26. Mardia, K. V. (1975). Statistics of directional data. Journal of the Royal Statistical Society B 37, 349-393.
27. Nass, M. M., & Cooper, L. N. (1975). A theory for the development of feature detecting cells in visual cortex. Biological Cybernetics, 19, 1-18.
28. Nowlan, S. J. (1990). Maximum likelihood competitive learning. In D. Touretzky (Ed.), Advances in neural information processing systems, 2 (pp. 574-582). San Mateo, CA: Morgan Kaufmann.
29. Oja, E. (1982). A simplified neuron model as a principal component analyzer. J. Math. Biology, 15, 267-273.
30. Pereira, F., Tishby, N., & Lee, L. (1993). Distributional clustering of English words. In Proceedings of the 30th Annual Meeting of the Association for Computational Linguistics (pp. 183-190).
31. Pérez, R., Glass, L., & Shlaer, R. J. (1975). Development of specificity in cat visual cortex. Journal of Mathematical Biology, 1, 275-288.
32. Tipping, M. E. (1999). Deriving cluster analytic distance functions from gaussian mixture models. In Proc. ICANN99, Ninth International Conference on Artificial Neural Networks (pp. 815-820). London: IEE.
33. Tishby, N., Pereira, F. C., & Bialek, W. (1999). The information bottleneck method. In 37th Annual Allerton Conference on Communication, Control, and Computing. Urbana, IL.
34. Torkkola, K. & Campbell, W. (2000). Mutual information in learning feature transformations. In Proc. ICML'2000, the 17th International Conference on Machine Learning (pp. 1015-1022). San Mateo, CA: Morgan Kaufmann.