Name
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. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1145/956750.956812 | KDD |
Keywords | Field | DocType |
certain application,computational advantage,different datasets,inductive supervised learning,machine learning,random projection approach predictively,random projections,considerable attention,dimensionality reduction,random projection,different machine,interesting theoretical underpinnings,supervised learning | Random projection,Data mining,Semi-supervised learning,Dimensionality reduction,Computer science,Supervised learning,Artificial intelligence,Computational learning theory,Machine learning | Conference |
ISBN | Citations | PageRank |
1-58113-737-0 | 103 | 5.51 |
References | Authors | |
9 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmitriy Fradkin | 1 | 344 | 19.25 |
| David Madigan | 2 | 358 | 36.10 |