Experiments with random projections for machine learning

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Experiments with random projections for machine learning

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Washington, D.C.

POSTER SESSION: Research track table of contents

Pages: 517 - 522

Year of Publication: 2003

ISBN:1-58113-737-0

Authors

Dmitriy Fradkin	Rutgers University, Piscataway, NJ
David Madigan	Rutgers University, Piscataway, NJ

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 17, Downloads (12 Months): 110, Citation Count: 9

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ABSTRACT

Dimensionality reduction via Random Projections has attracted considerable attention in recent years. The approach has interesting theoretical underpinnings and offers computational advantages. In this paper we report a number of experiments to evaluate Random Projections in the context of inductive supervised learning. In particular, we compare Random Projections and PCA on a number of different datasets and using different machine learning methods. While we find that the random projection approach predictively underperforms PCA, its computational advantages may make it attractive for certain applications.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 9

	Lance Parsons , Ehtesham Haque , Huan Liu, Subspace clustering for high dimensional data: a review, ACM SIGKDD Explorations Newsletter, v.6 n.1, p.90-105, June 2004
	Arnon Goldman , Israel Cohen, Anomaly subspace detection based on a multi-scale Markov random field model, Signal Processing, v.85 n.3, p.463-479, March 2005
	Kaushal Sanghai , Ting Su , Jennifer Dy , David Kaeli, A multinomial clustering model for fast simulation of computer architecture designs, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA
	Yoshiki Tanaka , Kazuhisa Iwamoto , Kuniaki Uehara, Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle, Machine Learning, v.58 n.2-3, p.269-300, February 2005
	Ping Li , Trevor J. Hastie , Kenneth W. Church, Very sparse random projections, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA
	Kadappagari Vijaya Kumar , Atul Negi, SubXPCA and a generalized feature partitioning approach to principal component analysis, Pattern Recognition, v.41 n.4, p.1398-1409, April, 2008
	Philipp W. Keller , Shie Mannor , Doina Precup, Automatic basis function construction for approximate dynamic programming and reinforcement learning, Proceedings of the 23rd international conference on Machine learning, p.449-456, June 25-29, 2006, Pittsburgh, Pennsylvania
	Ping Li, Very sparse stable random projections for dimension reduction in lα (0 <α ≤ 2) norm, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA
	Kyu-Hwa Jeong , Jose C. Principe, Enhancing the correntropy MACE filter with random projections, Neurocomputing, v.72 n.1-3, p.102-111, December, 2008