From learning metrics towards dependency exploration

WSOM 2005 - 5th Workshop on Self-Organizing Maps

Volumn , Issue , 2005, Pages 307-314

  Kaski, Samuel ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Associative clustering; Data fusion; Dependent components; Discriminative clustering; Learning metrics

Indexed keywords

ASSOCIATIVE CLUSTERING; CANONICAL CORRELATIONS; DATA FUSION TECHNIQUE; DEPENDENT COMPONENTS; DISCRIMINATIVE CLUSTERING; DISTANCE METRICS; LEARNING METRICS; STATISTICAL DATAS;

DATA FUSION;

CONFORMAL MAPPING;

EID: 54249101987     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

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2. Suzanna Becker. Mutual information maximization: models of cortical self-organization. Network: Computation in Neural Systems, 7: 7-31, 1996.
3. V. Ganti, J. Gehrke, R. Ramakrishnan, and W.-Y. Loh. A framework for measuring changes in data characteristics. In Proceedings of ACM PODS 1999, 18th Symposium on Principles of Database Systems, pages 126-137. 1999.
4. H. Hotelling. Relations between two sets of variates. Biometrika, 28: 321-377, 1936.
5. Samuel Kaski, Janne Nikkilä, Merja Oja, Jarkko Venna, Petri Törönen, and Eero Castrén. Trustworthiness and metrics in visualizing similarity of gene expression. BMC Bioinformatics, 4: 48, 2003.
6. Samuel Kaski, Janne Nikkilä, Janne Sinkkonen, Juha Knuuttila, and Christophe Roos. Associative clustering for exploring dependencies between functional genomics data sets. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted for publication.
7. Samuel Kaski and Janne Sinkkonen. Principle of learning metrics for exploratory data analysis. Journal of VLSI Signal Processing, special issue on Machine Learning for Signal Processing, 37: 177-188, 2004.
8. Samuel Kaski, Janne Sinkkonen, and Arto Klami. Discriminative clustering. Neurocomputing. Accepted for publication.
9. Samuel Kaski, Janne Sinkkonen, and Jaakko Peltonen. Bankruptcy analysis with self-organizing maps in learning metrics. IEEE Transactions on Neural Networks, 12: 936-947, 2001.
10. Arto Klami and Samuel Kaski. Non-parametric dependent components. In Proceedings of ICASSP'05, IEEE International Conference on Acoustics, Speech, and Signal Processing, pages V-209-V-212. IEEE, 2005.
11. G. J. McLachlan, Kim-Anh Do, and Christophe Ambroise. Analyzing microarray gene expression data. Wiley, New York, 2004.
12. Jaakko Peltonen and Samuel Kaski. Discriminative components of data. IEEE Transactions on Neural Networks, 16: 68-83, 2005.
13. Jaakko Peltonen, Arto Klami, and Samuel Kaski. Improved learning of Riemannian metrics for exploratory analysis. Neural Networks, 17: 1087-1100, 2004. Invited paper.
14. Jaakko Peltonen, Janne Sinkkonen, and Samuel Kaski. Sequential information bottleneck for finite data. In Russ Greiner and Dale Schuurmans, editors, Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), pages 647-654. Omnipress, Madison, WI, 2004.
15. Janne Sinkkonen and Samuel Kaski. Clustering based on conditional distributions in an auxiliary space. Neural Computation, 14: 217-239, 2002.
16. Janne Sinkkonen, Samuel Kaski, and Janne Nikkilä. Discriminative clustering: Optimal contingency tables by learning metrics. In T. Elomaa, H. Mannila, and H. Toivonen, editors, Proceedings of the ECML'02, 13th European Conference on Machine Learning, pages 418-430. Springer, Berlin, 2002.
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19. Naftali Tishby, Fernando C. Pereira, and William Bialek. The information bottleneck method. In Bruce Hajek and R. S. Sreenivas, editors, Proceedings of The 37th Annual Allerton Conference on Communication, Control, and Computing, pages 368-377. University of Illinois, Urbana, Illinois, 1999.