Experiments with random projections for machine learning

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2003, Pages 517-522

  Fradkin, Dmitriy ^a   Madigan, David ^b  

^a RUTGERS UNIVERSITY   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

Dimensionality reduction; Random projection

Indexed keywords

COMPUTATIONAL ADVANTAGES; DATA SETS; DIMENSIONALITY REDUCTION; MACHINE LEARNING METHODS; MACHINE-LEARNING; RANDOM PROJECTIONS;

DATA MINING;

LEARNING SYSTEMS;

EID: 63249106662     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/956750.956812     Document Type: Conference Paper

References (17)

1. D. Achlioptas. Database-friendly random projections. In Symposium on Principles of Database Systems (PODS), pages 274-281, 2001.
2. D. Achlioptas, F. McSherry, and B. Schölkopf. Sampling techniques for kernel methods. In S. B. T. G. Dietterich and Z. Ghahramani, editors, Advances in Neural Information Processing Systems.
3. U. Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, and A. J. Levine. Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays. In Proc. Natl. Acad. Sci. USA, volume 96, pages 6745-6750, June 1999.
4. E. Bingham and H. Mannila. Random projection in dimensionality reduction: applications to image and text data. In Knowledge Discovery and Data Mining, pages 245-250, 2001.
5. C. Blake and C. Merz. (uci) repository of machine learning databases, 1998.
6. S. Dasgupta. Learning mixtures of gaussians. In IEEE Symposium on Foundations of Computer Science (FOCS), 1999.
7. S. Dasgupta. Experiments with random projections. In In Proc. 16th Conf. Uncertainty in Artificial Intelligence (UAI), 2000.
8. S. Dasgupta and A. Gupta. An elementary proof of the johnson- lindenstrauss lemma. Technical Report TR-99-006, International Computer Science Institute, Berkeley, California, USA, 1999.
9. L. Engebretsen, P. Indyk, and R. O'Donnell. Derandomized dimensionality reduction with applications. In Proceedings of the 13th Symposium on Discrete Algorithms. IEEE, 2002.
10. T. R. Golub, D. K. Slonim, P. Tamayo, C. H. M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. In Science, volume 286, pages 531-537, 1999.
11. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer, New York, 2001.
12. P. Indyk and R. Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. In Proceedings of the 30th Annual ACM STOC, pages 604-613, 1998.
13. T. Joachims. Making large-scale svm learning practical. In B. Schlkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning. MIT-Press, 1999.
14. S. Kaski. Dimensionality reduction by random mapping: Fast similarity computation for clustering. In Proceedings of IJCNN'98, volume 1, pages 413-418. IEEE Service Center, Piscataway, NJ, 1998.
15. A. Magen. Dimensionality reductions that preserve volumes and distance to affine spaces, and their algorithmic applications.
16. P. I. N. Thaper, S. Guha and N. Koudas. Dynamic multidimensional histograms. In Proc. ACM SIGMOD, pages 428-439, May 2002.
17. J. R. Quinlan. C4.5: Programs for machine learning, 1993.