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KDD
2003
ACM

Experiments with random projections for machine learning

14 years 4 months ago
Experiments with random projections for machine learning
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. Categories and Subject Descriptors G.3 [Probability and Statistics]: Probabilistic algorithms; I.5.m [Pattern Recognition]: Miscellaneous General Terms Experimentation, Performance Keywords random projection, dimensionality reduction
Dmitriy Fradkin, David Madigan
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2003
Where KDD
Authors Dmitriy Fradkin, David Madigan
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