A bayesian approach to graph regression with relevant subgraph selection

Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics

Volumn 1, Issue , 2009, Pages 291-300

  Chiappa, Silvia ^a   Saigo, Hiroto ^b   Tsuda, Koji ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN APPROACHES; BINARY VECTORS; COLUMN GENERATION; DATA SETS; DUAL PROBLEM; GAUSSIANS; GRAPH DATA; HIGH-DIMENSIONAL; LINEAR REGRESSION MODELS; MATHEMATICAL PROGRAMMING PROBLEM; MODEL PERFORMANCE; MOLECULAR GRAPHS; OBJECTIVE FUNCTIONS; POINT ESTIMATE; POSTERIOR DISTRIBUTIONS; PRIOR DISTRIBUTION; REAL-WORLD APPLICATION; REGRESSION ALGORITHMS; REGRESSION PARAMETERS; SMALL DATA SET; SUBGRAPH MINING; SUBGRAPHS; TYPE METHODS;

FUNCTIONS; LINEAR REGRESSION; MATHEMATICAL PROGRAMMING; RANDOM PROCESSES; RANDOM VARIABLES; TARGETS; UNCERTAINTY ANALYSIS;

BAYESIAN NETWORKS;

EID: 72849150202     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. C. Helma, T. Cramer, S. Kramer, and L.D. Raedt. Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. J. Chem. Inf. Comput. Sci., 44:1402-1411, 2004.
2. J. Kazius, S. Nijssen, J. Kok, and T. Back A.P. Ijzerman. Substructure mining using elaborate chemical representation. J. Chem. Inf. Model, 46:597-605, 2006.
3. A. Inokuchi. Mining generalized substructures from a set of labeled graphs. In Proceedings of the 4th IEEE Internatinal Conference on Data Mining, pages 415-418. IEEE Computer Society, 2005.
4. X. Yan and J. Han. gSpan: graph-based substructure pattern mining. In Proceedings of the 2002 IEEE International Conference on Data Mining, pages 721-724. IEEE Computer Society, 2002.
5. S. Nijssen and J.N. Kok. A quickstart in frequent structure mining can make a difference. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 647-652. ACM Press, 2004.
6. B. Schölkopf and A. J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.
7. R. Tibshrani. Regression shrinkage and selection via the LASSO. J. Royal. Statist. Soc B., 58(1):267-288, 1996.
8. S. Morishita and J. Sese. Traversing itemset lattices with statistical metric learning. In Proceedings of ACM SIGACT-SIGMOD-SIGART Symposium on Database Systems (PODS), pages 226-236. ACM Press, 2000.
9. K. Takabayashi, P.C. Nguyen, K. Ohara, H. Motoda, and T. Washio. Mining discriminative patterns from graph structured data with constrained search. In T. Gärtner, G.C. Garriga, and T. Meinl, editors, Proceedings of the International Workshop on Mining and Learning with Graphs (MLG), pages 205-212, 2006.
10. B. Bringmann, A. Zimmermann, L. D. Raedt, and S. Nijssen. Don't be afraid of simpler patterns. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), pages 55-66. Sprinter, 2006.
11. A. Demiriz, K.P. Bennett, and J. Shawe-Taylor. Linear programming boosting via column generation. Machine Learning, 46(1-3):225-254, 2002.
12. G. Ratsch, A. Demiriz, and K.P. Bennett. Sparse regression ensembles in infinite and finite hypothesis spaces. Machine Learning, 48(1-3):189-218, 2002.
13. H. Saigo, S. Nowozin, T. Kadowaki, T. Kudo, and K. Tsuda. Gboost: A mathematical programming approach to graph classification and regression. Machine Learning, 2008.
14. D. Wipf and S. Nagarajan. A new view of automatic relevance determination. In Advances in Neural Information Processing Systems 20. MIT Press, 2007.
15. D. J. C. MacKay. Information Theory, Inference and Learning Algorithms. Cambridge Univ. Press, 2003.
16. D. Wipf, J. Palmer, and B. Rao. Perspectives on sparse Bayesian learning. In Advances in Neural Information Processing Systems 16. MIT Press, 2004.
17. S. Nowozin, K. Tsuda, T. Uno, T. Kudo, and G. Bakir. Weighted substructure mining for image analysis. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2007.