document: 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 .
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