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

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Dmitriy Fradkin, David Madigan
• Keywords: Data Mining, KDD 2003, Keywords Random Projection, Random Projection Approach, Random Projections