Dimension reduction

Cognitive Technologies

Volumn , Issue , 2008, Pages 91-112

  Cunningham, Pádraig ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; FEATURE EXTRACTION; LEARNING ALGORITHMS; MACHINE LEARNING;

DATA OBJECTS; DIMENSION REDUCTION; DIMENSION REDUCTION TECHNIQUES; HIGH DIMENSIONS; MACHINE LEARNING TECHNIQUES;

DATA REDUCTION;

EID: 77957659350     PISSN: 16112482     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-75171-7_4     Document Type: Conference Paper

References (48)

1. C.C. Aggarwal, J.L. Wolf, P.S. Yu, C. Procopiuc, and J.S. Park. Fast algorithms for projected clustering. In Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, pages 61-72, 1999. http://www.sigmod.org/sigma.
2. D.W. Aha and R.L. Bankert. A comparative evaluation of sequential feature selection algorithms. In Proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, pages 1-7, 1995.
3. G. Baudat and F. Anouar. Generalized discriminant analysis using a kernel approach. Neural Computation, 12(10):2385-2404, 2000.
4. R. Bellman. Adaptive Control Processes: A Guided Tour. Princeton University Press, 1961.
5. A. Blum and P. Langley. Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1-2):245-271, 1997.
6. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
7. K. Bryan, P. Cunningham, and N. Bolshakova. Biclustering of expression data using simulated annealing. In CBMS, pages 383-388. IEEE Computer Society, 2005.
8. S.C. Deerwester, S.T. Dumais, T.K. Landauer, G.W. Furnas, and R.A. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6):391-407, 1990.
9. M. Devaney and A. Ram. Efficient feature selection in conceptual clustering. In D.H. Fisher, editor, ICML, pages 92-97. Morgan Kaufmann, 1997.
10. M. Doyle and P. Cunningham. A dynamic approach to reducing dialog in on-line decision guides. In E. Blanzieri and L. Portinale, editors, EWCBR, volume 1898 of Lecture Notes in Computer Science, pages 49-60. Springer, 2000.
11. R.C. Dubes. How many clusters are best? - an experiment. Pattern Recognition, 20(6):645-663, 1987.
12. J.G. Dy and C.E. Brodley. Feature selection for unsupervised learning. The Journal of Machine Learning Research, 5:845-889, 2004.
13. D.H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2(2):139-172, 1987.
14. R.A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7:179-188, 1936.
15. K. Fukunaga. Introduction to Statistical Pattern Recognition. Academic Press, Inc, 2nd edition, 1990.
16. M.A. Gluck and J.E. Corter. Information, uncertainty, and the utility of categories. In Proceedings of the Seventh Annual Conference of the Cognitive Science Society, pages 283-287, Hillsdale, NJ, 1985. Lawrence Earlbaum.
17. J. Handl, J. Knowles, and D.B. Kell. Computational cluster validation in post-genomic data analysis. Bioinformatics, 21(15):3201-3212, 2005.
18. J.A. Hartigan. Direct clustering of a data matrix. Journal of the American Statistical Association, 67(337):123-129, 1972.
19. X. He, D. Cai, and P. Niyogi. Laplacian score for feature selection. In NIPS, 2005.
20. X. He and P. Niyogi. Locality preserving projections. In S. Thrun, L.K. Saul, and B. Schölkopf, editors, NIPS. MIT Press, 2003.
21. D.R. Heisterkamp. Building a latent semantic index of an image database from patterns of relevance feedback. In ICPR (4), pages 134-137, 2002.
22. H. Hotelling. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24:417-441, 1933.
23. R. Huang, Q. Liu, H. Lu, and S. Ma. Solving the small sample size problem of LDA. In ICPR (3), pages 29-32, 2002.
24. A. Hyvärinen, J. Karhunen, and E. Oja. Independent Component Analyis. John Wiley & Sons, Inc, 2001.
25. G.H. John, R. Kohavi, and K. Pfleger. Irrelevant features and the subset selection problem. In Proceedings of the 11th International Conference on Machine Learning, pages 121-129, New Brunswick, NJ, 1994. Morgan Kaufmann.
26. R. Kohavi and G. H. John. Wrappers for feature subset selection. Artificial Intelligence, 97(1-2):273-324, 1997.
27. Y. LeCun, J. Denker, S. Solla, R.E. Howard, and L.D. Jackel. Optimal brain damage. In D.S. Touretzky, editor, Advances in Neural Information Processing Systems II, San Mateo, CA, 1990. Morgan Kauffman.
28. D.D. Lee and H.S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788-791, 1999.
29. W. Liu, Y. Wang, S.Z. Li, and T. Tan. Null space approach of fisher discriminant analysis for face recognition. In D. Maltoni and A.K. Jain, editors, ECCV Workshop BioAW, volume 3087 of Lecture Notes in Computer Science, pages 32-44. Springer, 2004.
30. J. Loughrey and P. Cunningham. Overfitting in wrapper-based feature subset selection: The harder you try the worse it gets. In 24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2004), pages 33-43, 2004.
31. J. Loughrey and P. Cunningham. Using early-stopping to avoid overfitting in wrapper-based feature subset selection employing stochastic search. In M. Petridis, editor, In 10th UK Workshop on Case-Based Reasoning, pages 3-10. CMS Press, 2005.
32. G.J. McLachlan and T. Krishnan. The EM Algorithm and Extensions. Wiley, New York 1997.
33. S. Mika, B. Schölkopf, A.J. Smola, K.R. Müller, M. Scholz, and G. Rätsch. Kernel PCA and de-noising in feature spaces. In M.J. Kearns, S.A. Solla, and D.A. Cohn, editors, NIPS, pages 536-542. The MIT Press, 1998.
34. D. Mladenic. Feature subset selection in text-learning. In C. Nedellec and C. Rouveirol, editors, ECML, volume 1398 of Lecture Notes in Computer Science, pages 95-100. Springer, Berlin, 1998.
35. D.J. Newman, S. Hettich, C.L. Blake, and C.J. Merz. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository. html.
36. A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Proceedings of Advances in Neural Information Processing, 2001. http://books.nips.cc.
37. A.Y. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems, 14(2):849-856, 2001.
38. S.K. Ng, Z. Zhu, and Y.S. Ong. Whole-genome functional classification of genes by latent semantic analysis on microarray data. In Y.-P. Phoebe Chen, editor, APBC, volume 29 of CRPIT, pages 123-129. Australian Computer Society, 2004.
39. J. Novovičová, A. Malík, and P. Pudil. Feature selection using improved mutual information for text classification. In A.L. N. Fred, T. Caelli, R.P.W. Duin, A.C. Campilho, and D. de Ridder, editors, SSPR/SPR, volume 3138 of Lecture Notes in Computer Science, pages 1010-1017. Springer, Berlin, 2004.
40. J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco, CA, 1993.
41. J. Reunanen. Overfitting in making comparisons between variable selection methods. Journal of Machine Learning Research, 3:1371-1382, 2003.
42. M. Saerens, F. Fouss, L. Yen, and P. Dupont. The principal components analysis of a graph, and its relationships to spectral clustering. In Proceedings of the 15th European Conference on Machine Learning (ECML 2004). Lecture Notes in Artificial Intelligence, 3201:371-383 2004.
43. E. Sahouria and A. Zakhor. Content analysis of video using principal componets. In ICIP (3), pages 541-545, IEEE Computer Society, 1998.
44. P. Smaragdis, B. Raj, and M. Shashanka. A probabilistic latent variable model for acoustic modeling. In Workshop on Advances in Models for Acoustic Processing at NIPS 2006, 2006. http://www.idiap.ch/amac.
45. M. Sugiyama. Local Fisher discriminant analysis for supervised dimensionality reduction. In W.W. Cohen and A. Moore, editors, ICML, pages 905-912. ACM, 2006.
46. M. West. Bayesian factor regression models in the "large p, small n" paradigm. Bayesian Statistics, 7:723-732, 2003.
47. L. Wolf and A. Shashua. Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach. Journal of Machine Learning Research, 6:1855-1887, 2005.
48. S. Wu and P.A. Flach. Feature selection with labelled and unlabelled data. In Proceedings of ECML/PKDD'02 Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning, pages 156-167, 2002.